{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本文将采用 `Inception-ResNet-v2` 模型 完成58个类别的接近6K张交通标志图像的识别，重点是了解 `Inception-ResNet-v2` 模型的结构及其搭建方法。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一、前期工作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "🔥本文 GitHub [https://github.com/kzbkzb/Python-AI](https://github.com/kzbkzb/Python-AI) 已收录\n",
    "\n",
    "- 作者：[K同学啊](https://mp.weixin.qq.com/s/NES9RhtAhbX_jsmGua28dA)\n",
    "- 来自专栏：《深度学习100例》-Tensorflow2版本\n",
    "- 数据链接：https://pan.baidu.com/s/1NMwgF0Qux5T4MCNYQo21dQ （提取码：x9ap）\n",
    "\n",
    "我的环境：\n",
    "\n",
    "- 语言环境：Python3.6.5\n",
    "- 编译器：jupyter notebook\n",
    "- 深度学习环境：TensorFlow2.4.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 设置GPU"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果使用的是CPU可以注释掉这部分的代码。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "gpus = tf.config.list_physical_devices(\"GPU\")\n",
    "\n",
    "if gpus:\n",
    "    tf.config.experimental.set_memory_growth(gpus[0], True)  #设置GPU显存用量按需使用\n",
    "    tf.config.set_visible_devices([gpus[0]],\"GPU\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# 支持中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "import os,PIL,pathlib\n",
    "\n",
    "# 设置随机种子尽可能使结果可以重现\n",
    "import pandas as pd\n",
    "import numpy  as np\n",
    "np.random.seed(1)\n",
    "\n",
    "# 设置随机种子尽可能使结果可以重现\n",
    "import tensorflow as tf\n",
    "tf.random.set_seed(1)\n",
    "\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers,models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入图片数据\n",
    "pictures_dir = \"D:/jupyter notebook/DL-100-days/datasets/14_traffic_sign/images\"\n",
    "pictures_dir = pathlib.Path(pictures_dir)\n",
    "\n",
    "# 导入训练数据的图片路径名及标签\n",
    "train = pd.read_csv(\"D:/jupyter notebook/DL-100-days/datasets/14_traffic_sign/annotations.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 查看数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "图片总数为： 5998\n"
     ]
    }
   ],
   "source": [
    "image_count = len(list(pictures_dir.glob('*.png')))\n",
    "\n",
    "print(\"图片总数为：\",image_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file_name</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000_0001.png</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000_0002.png</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000_0003.png</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000_0010.png</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000_0011.png</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      file_name  category\n",
       "0  000_0001.png         0\n",
       "1  000_0002.png         0\n",
       "2  000_0003.png         0\n",
       "3  000_0010.png         0\n",
       "4  000_0011.png         0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 二、构建一个tf.data.Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.加载数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据集中已经划分好了测试集与训练集，这次只需要进行分别加载就好了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_image(image):\n",
    "    image = tf.image.decode_jpeg(image, channels=3)  # 编码解码处理\n",
    "    image = tf.image.resize(image, [299,299])        # 图片调整\n",
    "    return image/255.0                               # 归一化处理\n",
    "\n",
    "def load_and_preprocess_image(path):\n",
    "    image = tf.io.read_file(path)\n",
    "    return preprocess_image(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "AUTOTUNE = tf.data.experimental.AUTOTUNE\n",
    "common_paths = \"D:/jupyter notebook/DL-100-days/datasets/14_traffic_sign/images/\"\n",
    "\n",
    "# 训练数据的标签\n",
    "train_image_label = [i for i in train[\"category\"]]\n",
    "train_label_ds = tf.data.Dataset.from_tensor_slices(train_image_label)\n",
    "\n",
    "# 训练数据的路径\n",
    "train_image_paths = [ common_paths+i for i in train[\"file_name\"]]\n",
    "# 加载图片路径\n",
    "train_path_ds = tf.data.Dataset.from_tensor_slices(train_image_paths)\n",
    "# 加载图片数据\n",
    "train_image_ds = train_path_ds.map(load_and_preprocess_image, num_parallel_calls=AUTOTUNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ZipDataset shapes: ((299, 299, 3), ()), types: (tf.float32, tf.int32)>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将图片与标签进行对应打包\n",
    "image_label_ds = tf.data.Dataset.zip((train_image_ds, train_label_ds))\n",
    "image_label_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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g6pR0z9ifOZL1ha0q9g52ecO/wXAwuLY5nI4nure93d9XzOmVBfAfjQeMxlvrPaE9bPyiShBeYK5tchmTbjLrhdj/Lv9WoYueRhschtrUiBiiKk3Xsurm/fkdY4dgEtss36O1FpWY9roKvos0ywaRBLqVIFVdVcnAFE37B5L9BGiMVFWFUcOqaUCUne1dFEfj2wTSxYAGjwiMhmPatsOHjijKcDCkMjWVSyl33gdC6OjajnrgEDWsmiWD4QARiw8dH3/04FoZqDdyIzfy374YY9Q529tvIuTUWCVEZXu6zaByWTdB0YSFwasaWa0WdF1KHfYhggiDwQBB8V1HwLCzvYOzptfNihJ9i66WsGwwYZ3nkb7lzyYv6PF/0RvZcJOrCh2PkbrKQXDNbOONO9lkZeQvWo/DZy68/lD/0U2n9/jkhNlsfm1Gjgj5TCmGmsF3ySETZ1MQ1+RzzZqUNhKh8x5nlP3pmOenFg2BkTHUxhB8QBQqZxiqo4sdIcLJvOGffe8DPnz4jL/6a1/ny7/5Dt1PT2k++pTx6pKaFkPY8GVAejZGX2Sh9zP6wiwbkdre7SifL6OX7Q9FiRgadSztgNXLt3F/6Sv4e3fwbkAthtpACF0+dx2ucn3Qylqb6tVmMkT6EoMak8czeb4+hDS2Wu7DpPPYCNb9/On7/KBNXE9kLoqBSMqZj8UDlDyQPRqw4V6aDedqwyCEfgmuBz17YOuKE/nFuL6eZADHiGKs4KylqivqumJQ19R1TV05xDpEXE5DiATf0TUNbdfStW0yipqWNkR80B60IDvehckTYZ3erwmYKNFCJY1FpKSDKZjy+YTcqSTAwiBYDDGScvQl1RTJF+mBhet2MBJrIGYjTdfkjB6JkJ5RkYCxmOqSSMwOAGxGbTU//6CyXOWof6E1xfx7zNoJTiCN0LM/NIMvJkVZlaIbBEthyKxBNsoaQHtFl0ss9RtEtTwp6TtIz1HmU8igDppXF/2z0N8fPbCzUWYlgxTp+VRiZmfRM2xMrodSQJCIYFTWe+NaZQNK6FNV6CdURBBrCT0rsaRSaYqyQ15ra9ciL+u07vJUlnSz4jgZAStCyHODxB4cWSNrOcq/HuGkJ3WtSFU20ivz56LEnn2XPq09C6s8cgF6y7PqxuoowFk5HF9kh62VecnjKnCP5DHbZOFIKeBc9ND1Em3QqMSVx9SCrUyOVCR2WqoFZNMRWRkqZ9EY6eYN0Ud8m1hrBsAolatRwMdAiGl+jUsHQD1Izl/wCeyJXonEpAeDxVmLMYIag7NpftqQR0cMxpkEtGQnHomolL0eUDEEk25mWFUQHTF4mhgxlUsAmTFo1HVaVVmvGUkvDDrZmAvytJdaZ5WrGDpHow3Bx2Qr5MMldElPlZpOicUYiSEBYsYIRi3G2Gvfi/Plgv/0H/7nNMsls+WMwaAmmBZjarb3d/j617/B+dOn/OFP/phn549BAqvlkvnVkufHpxzeucXhzi6n58fEoAgVi1mHj57ajpKxqko9HIBGnj18gBkNqCNcPHpEe+cWg6MD3hp/C6zj2eOn+G6BquJXC2aLioBBCUS3ZFldItHQ2iuawQnSdcTgqeyUC7nADCqqyjEa1KCRED2BCFE5efQpHz59yNHt17k3mWAAW9UQIsF7xJhkDwjUg5r97SniG8a1I0TFDUdITGtPywSnzZDWAwlRLYDsOrgCKhZBsa7GSUWM1zeP+3t7/K/+Z/8OftWyala0bUvTNQSUruv46lff4pd+9a9gTbIhpFsioUMJWadl4F4VYuyZR5oZtCo5AunqFMzpjQjwBLrYIdHy4Pwpj2cf8tLkLldtoK7BiqNtO57NP2EeWo6fPyG0DdvjXS7nz2kXHc+ez9m/s4MRMENL27WYztAuWoIsGY23qZxDNFKNh3SdweBxpkZNoO08F6dzFvMVhwdTXrr1RX760U+4de+AX/vaX+PyySnff+8HnDXnXF6dsb23xeHBHluDW7x57z4fPfyAk6tP+Nov/zpfe+WbjOspYAldhxg4PTlmujNGg0Oix1iDDx4h8jf+1r/98bVN5I3cyI38hRDnLLcO9yEGjBiMsezs7VJb5eKq4a//1t/knVfvUjnJQaXE2DCVxQB+NeOH3/9Dnh6fYozhYjYnmAFvffGLSLvk2dMnzOKQf/1v/msc7W3hzIZfQmRxdcry8UfoD/6Y0ftPqZaCMxZjIqZYoTmIm4JI2YOWwq6m98dijBuOkvR+XAJgNNdyTDZiRGkFVjtj+MoX4Z2vwO4+dV2jGhIr2rhUay7b14kVm+AD1QS826pKbOoQ0BjzGZPsvt56NQYxghPXn5f/6//N//Z6JzIDMirS+7mEDis+2dG5zAEYgsZcRkTAJLvSGagRvAo1Ql1bWmsT4CEwqiuaEFhpYmM3ET46mfH3/tHvcfwr5/ylb/0a4zdeZvWDh4weP2TYnOO0wRIwfTQ8Z1okj2sDS6D3GXp/STI0EdPgq6yJJlENHY6VG7G8c4h8/U3sO6/SDScglqF1iVlkDCF0hBByRgM9EGfFEMX2wycCpnKIcagKIXSIKC5mlCBq/nzyW4SItT8fmvmcoI1kmvTGgJRIeJ7U3sntHapidG/gAj/jyj3npaRIaA+ZZABDM8iT3y8JMDHAYFyxNZ0ymm5Tj6eYwQhT1Tg7xIhJbqMmuzDl3CdnzISOlEbTEbuG1WLBaj5jdnXFfLZi0XW06R2ZBk5+sEhJTREpeWr2hfs0vZtYnoL0mT7Vh1RXIAMjm1lnCecq771m0EbS05v8KH3KWWZQpcdKXn4UUk2UCAWaLPPYbwIBNUI1GOCXDSGAs6yBNZPntaQfsU5DCTHVJkE27qOAHqxrrKiwjsjn5KUXx6UY9vLCK6BrAznfvRJfHFEpS3h9/QI0rhGLBFiVFLo1eyPfX3bwC+AkmtN3dP3d147ZrPVRf7v9AsqAhxEBY9BAfx+JcWR6sIPymHlZr5lPa6eiPIBoWgU21zRJDvEa7pFeD/RnJyUKnG5zA94uf0oqVj8+hYlWdEfmqJU5zLcUlRzhz2CqxBcKEZe1XcCdsl5Lge+yCk0PGuX3ZCCpLG/duN0Xk76uQbKesM5gawGbOnNFVZxxqKYoOyLgLKFVNEQ0ZKgpP6tkXUiMYMnBfCX6SOigrjNw4iPRRzQEjIlUo1R3JqBYLNYKBosx4H0gxhS9d67KdWOSQRHRXExa0JCOy2AUJSBWqOuKGAznF1fUgyoP3hokTIBuzD8mJ99EenCxMDSLARXzc7rKMhgM0BBpJSASMSIYZ9BQwHHBmhTtCV2H+pjXg4AYLDYFFK55Io9PnjAcj6iHNTujbearGd61zJYzHjx5xKOHH9PEDm0i4+GE/YMDfvmtQ373u79DNahxZoCKwzrDsm3ZO9hn11Z88ukTmlUDVnCVo13M+eDjHzM5POLe7hFbW9tcXV0y2tpitLNNNRyztbvP5fOnfHhyyny1ZG97F2OFaDsac8aiu2IeL+jikqEcYvwljS4YDSKVq7FVKjDsQ8equ+S8fcrz5iHTwV2a2Yzv/PM/pj54wF//a3+Tt0YTgve4qsbE2DvpCRyOzOeXaF3TBmgjDHJaZY+36EYKs4LRiIjN6lez/sibZcPo2j86YlDX1zaD2XIhiiTGaQYAJa/dEHxK+ZWSTEkyZmOxT9JZETUirk6pt9lwlZKGS2IhlTTkBLIqXQi0oePHz37Co9Of8sWjexAC84snPFqcMd4eMYuei/kFbbOiXTYonjCMjNw23fKM2glXZ3Mm0wk+wGKRNtPspMNYTzVyrOaeZnaFj2eMphVVPWS5OMcYQdQwv5hzcXrFanXBQjumt8bcO9xlYOHe/ZcZGMt/+U//K54+fsr9u4537n0RG7fZ396mmx9wd3vCk/c/YlRN+Nr9X8Yag9Q1qsre/lECaCvTp5oa62HDuL2RG7mR/25JH8IrdfA0B4Mz41eMzW/KvmVmcCMgxlBbhyUxhd2gxtoBlXUois0OslEwOSAUQ0xsDyzj8QHDV7dY7N1h+dpPaH70AfWTM9wq4LSUDlj3xejLAJSMCpE+8KiZqZzcg2IwkwEAQ1DFq+JFaaYjutfvw1e/SnXvJQbjMUZLrcEMGuVAGfmeTWEf93aWYrLjbgHJdWViDv73frCSbbJsg4vZGPVrnEMFHzyYClSJPiSAIWfcIGBt9o1iCqbVlSUCg7pma3eH85MTiErlhNrafqzVGGQ8YtH6FCwUw/7RPlezOX/y3ifcurXHl7/2K5hXvsni4X1WH3xK/eQp9dUZtlthNWCJCNmm3PAFNfvom2OSwLU0TkqqWemNxbshzXBMe2sP3rwHr75E3N7CjUaMqqq/xppIUaHGUdnsJ5Nwi8KQXyMxKe0Jk0gaRlwCmzJgGNbpIclv0x5S/JnyuZk2pX5I+mvt9PWTSzGATI9CQjFRNhxr7ZnQG4swzX7cvJiC0Y0uUiRbqXaGydaE7f1DRtt72NEOYDMwo6j3+GUDvgXvMZ1HY0hfFkNicFlJxlldIYMR460pk+1D9m972tWS+eUlp6fHqeV2DGvGAuvYfpQCARWHORslsq5v04MVG452VPqCnkEhFCXRozeFA3DN4f28iMomV02gVj8txZrOAFxfU0RJ+XcbEyak9CdBqKuKcLVItHKbHKOSwhazF50M4PVikQyiJAWZR0bXIFkpPpv0ZvmdWQ9igh965kt/YbQHTlAy6JTXoK7nsPzVdwfqdXHs8TLNH1hnPCZHcg09lXvTDB7klzam7UWVcU3SAyEba3JTm0Na28YSg6JGkHxTKc6g/Zysa8is73edl7je1UpySkqHKNVEcZSYwY5M+4+Z6WHL3s8sp5KmBZJZWOVQ3LhlcpliXSMmWhx9VcgxEukrGpW3JQZUXrZYioNFxknLQbxmzvUpgJn22UfFM5ia/iSmULnfa5XM1opic8TEJFZajPgMDopNXYKCCm0TiAGcM5haKAV9jROGWzX12BFtol22K4+PinNCVWnSfcQEag0sGg2usnSxSwBNBm5U08FnBGzlsNZhjN149hQR05gMhRgV7QKmSUWvO5eiJ9bYPDEGYwEspfuYamJapCLuiT2BrAG9CBiniEs1aIL3hCj41uHFJvCpDYSVx9YVxqaDM4SY2Bch1T8RBOsc4ko0LBlWes27URWaZYuxFdvTKb7pOH56ihsMOdy+ww/f/RGPTx4z2RkzcJZFs+C1199kb7TLl19/i48eP+Pkco5IzWQyBgO39/cYqeH98JgQMzAaIz/48Q94ePyI25Mxr95+mfZqhg8e37WISdGr1ndEW4FxTLe32dvd5/zyHBMGDFaHdFiez09orCWMVmztVByOv8j++JDT44dYEbQOzOwF7z3+Ix48+x6zbsmtw9c44hUWs5ZPTz/kjS8+5q2795MB3nXYymLqGvWhN75XyxVaDfAxZaRXUfDRQz4L0v6LeX0VHR9TsEWktw2kgL/5ELV1jXOf23z5F0xkupeiUUUEfNJrIYYesImq61RY49LZYQwiaf+omN62T2dsASOh1Oojg58nF+ecL59iXM355UfUJrCIgat4zEKWjAaOw+EBF88/JjQzUKjHA87PL+FsyOmTGaJL1NRcnM9pVsp4VDE7nbNcNtSuwkwcz59fcnFxyaBWdrfGGHUszhcslzOGdQ3iCLFjOHFYoF22PLxo2Bq+Shse8Na9u9x7/S2Gf/A7/PovfYVf//W/yvPTwJ17+xzPW6r9O9w7vMOt8zOo/Asp5jHGnO+fNks+DQhBaUN7rVNoSuHMDRFS1D8Z64G97SlVNaA4iuU9ZW21nadZLYFI6xOFva7rPt3LaGR7e6d3qjSfvbFtYDHHdD7XuPuXO/c33/NngZN7i9GADhyMxkRX99kMhfUl5R+f/RLZ/FF+9q/lxScptn+IStd5Li8vWC6X16JY0xyWIGGao6qq6LqOejBgazJJTtLGPa1TSZTlckEIHgU6HzDGUGeAt207qioFec0G4xOUGD3atuhqBa1HQuytvI1v+pxP87NmdO0v9HNnLToawHCI2DWgkOoAZgBZ/rQt2Vu98jPuq9hPxdnM4Ej2LvOvkz3Uti0+Rk6Oj681VdFk/yipdoVQMA8lxEAMAYwFk9adtdmuQRGxKQATIyIOV9VEkvOc0jpjTtUm+5GhR2CUCFYQO2C0fws7nrI8us/Vw48xDz7FPTnFzRpsAKeCU8ViUraDWbv9xS/NYf50BuSabYrgo+Ct4AeWbmdCfPke+uabuDv3GG7tUA+G6R5DqhkXYsoIwUjP0OhLaEiyO0OarXTOxAgxYlMb18QIyb6NtRZnDCF2FIZxHwS9Vlmvn5RqrkQxiTmbWVIJu1FcceolpU5FFaxTdrannJ9foMGXiCtioDJCVTl2B0PccIqb7tKamtWqYbV4wMW84cfvfsCdo0Nuv/xF/L1twu5rNBeHyPML3Okl7vISt5hjV8useyNGi8eSbX4h1ahUsv9ik44cDvHTMd3OFuFwH+4dobf2iMMBYi3TyYSqrtP6imlmyEFwIZVpMJK6dab30PtOxUcUKdkdsfe5JGYdmvcoeb0bI2iIv9Dr//xWTyxFTbUHb35WxQ5DMVDK4BWlylpv9EsiO1LZU1I1GcXLTgbJmxZR6sqwd7jP1sFt6vEexg4THauLxGZBbFawamG1gHaJ8R3SrhDfpfswJg1eKa7oHNE5GE4QV2FcjR0OGQ7GVEcTJrv7LOYXHB8/4/z8iibT31LhXklRQS1MgPIc5fBbn9x99B5yLRZJLAggaEQJ9PSXntWi9EVZrkmMCLYYv7I+fEs6TGKMbBRzylolFrZDNjqFmAAKCxJgNKzxMRJiBnaygRAL7aI8fn481QzW9KBQzEDL+iAtzKaswtl0t3oIJb9nnZL0p5+5pMr0/2ZjxZaDMJ+exXlIa1J6gGb98fRDzLcteZDKWi+IQM9eya9dO0tj07jKAESPWFAcHahEaDOAkSTlBZex7EE7yp580Wkpz1AMQLAYibieHrp+XzECDD1xq19LSNovJispKdWB+4co475ONBNZ00DRAjRtvjv2LCzNyrN/w8Z1e2AQkP666d5jBpAQSdEaXc9bSs0tNao2r3lNotBetXTLgNjEgFISfjHZG6faNp3SLrvUWccoblgxGjuqgQME7yOdeoKBNkc5YlCMCqNhRTVyiAHfBAzgTGKbzK+WtMvEpkmHLwymDmchtIqtKzQmcCeGxKgBAZsHKIIE6cHJbtFhjaWznmElDEcDnK0IIVDVgmAwMRCC4n1MdlqJtsRiCOVBkYialGQafCB0ESuWytVsjce0wdCajkVoEiBnDaaqMTbXSgGQgDGCrQVxkWwZESXw+Q3vXyzBB3wXaRcrjq+WWGuwVvjG177Ojhvw0cefcLR1C2PgYn6OVMp3fvSHfPXtrzGd7rM7WfHg8gRjYTQaM9kbo2bF2XlDDL4HLcJixbd//3fR0YD7b36V0HUppaqyNMsl9daUajylWy3puhYVS22HVMMJ28Yx3NnjzmCEscorzZe5WF4RxEPt2BrtM7u85KJbIBPw1nN2eYF+umB+0RCXsHBCN1Lq8ZAtZ6mMWzsLmmo0iTWYqkoObN5XPgawiq0GqQxSTAENk3VrVNMb8uRnTamn0teD650cLeGG696Om6C86ZmiBYTXEInBk8BHUOcQqTI7SPMJmdLyik5MV9P+nC12jKpy1S45X53y7PSY+eoJu1s7HE73CV3DbDbnvDnh8mLGyA5ZeoPYipHs8PjymKCWZuVoF1dUwGxmePzJM6IIbXfG/bfvsH1rl3h8wvnzGbqyrOYNoxHsvb5LqCvaEBLI2ioaI8asEImMRyOqyuFXnsXxFav6E+TNmk8vxvzoo2c8vzrlOFQ8/If/NaNRYPrJmIPtbb587x0uz44Bx4OHz9nfe4Wd4fYayA+BJtcAqEQQDSwuL/ndf/4n1zqLKSVjDyFHsEWorGF/dxvcgNn5Gf+Tv/mXuXPvLcRUSKbwV5XFiCVE5cGzY95/93sIHU/Pz1ENvPbGa5hqxPGzUwZ+zr/+N/41htu3Ung519FqmobV6RPij36A+8n7jE5nDDRXQBQyPL25LmIaH02p8Vpe31iTvXFSAqTlb0xK3UNoa+huj9GvvAGvv4NMboNxtKHDiMEZl5wtwFmLEpITE5M96KoqrfUMUmi2jWP+2dhsWxQwIXc5VJSTqwUPHz3l7/6d//D65tBabu/vIapYa6hczZ27t3n09Bkvv3Kfv/Wbv8m0HiTHT1IdCGMNziY9+IPvfZfTsxOwhrP5AhkMee3Vl+hWHY8fPmX/6DZ/9S//FqPRcD3+GQQI3YpmdsrswQfE99+n+vg5g7nHUZh32XnNzDpTbNRSmo/eZyNkn6aEl9YpGCl40InQ7Uzo3noJeetNhi+/hhuMsUZyWE3oug5rBJdB4RAj1tnUbVZSN0vVdF5DYsgamxjeGmOJDmf7LOuibCfGqDRth4bIT3/6IScXM/7u3/kPPr62iaQAfvRLOX1/+l0K4KSAYhmgqKlGZh+UrCS1d1HFGkvKQMip6hIJMfkhIYciTfZrRAyl3IG1NYOB4EcNzyaH6DsHmDdXuLMThsfHVKdnuKsltu2wIbNH1lgQEjXZmqT0nahKdIKOasL2NvHwEH3pNnr7NrK1y2A8ZTydpPSnEFNHUAUNStSID2mOguamCzHm1KO0uKJIArljZkWL9Da2tev6rNZWiWkcSsA426nXjdrkw9ZgaGNKg4o5BViL45SZTkYzi1/BSj6vY2Bna0g9HNPOrtAYcknEiDPCaFSzf7jH7fuvc+f+64TRHv/vf/Yn+AcnNH7GTx6cc/D9HzMe79DFCVdnxxha2m2QyZQxOwwD1K3HNgFWHmk7bNciPvQ4RXSWWDm0cuhgCOMR7EwxB9vY3W1kNMw1eiwmu+NWDNbkLJqe8Z/r8WUcIWpKq0pgUF7Z+byXovxL0EnJXWGzajcF20h1bY2RnGHy862bzw3alOj1hke/XiMbPljv7EGfKri5mkp+XEJU14BO2mbFRTcUZMoZy87uhL2XXmawdRuihRDRtiEsltAswLcJoOk6pG0Q75HgkXaZACCNvREhOYSnNhUI0uU8vyb4eoDUQ2R7i3o0wW7tM5zusndxztNHj7hYrQgZaUvdqgwZfksOsBaELVPtegc2R/6lOKgZ3Akh1cbpldnaEF5rveuRFDWMKC4r0vIc/RuA0jmnoEz5dzEX7c0Hlui684cak1ItYmlrXhZB0SLpc33lglzItKDqZWOZ9YdJLPWc8pIjr0I+KEMy/mPZOKRuUYE1XT0Z//2KzOMZc8Q/fYdmoCal96SBKMyT9LEN5ldh5JTDJrNLJOZxzfdeUO9SryWh0NetSTcnZj3WmxEXJUUkXmh/XJ6bzGgRk6dc18BEr2TyWi2bWUA0IhaMS/mrWnIfc6cuKQWAMsCnuTgplLQjSYevkT5X1pTDKkRKtyGNkoyODWZEGcu0XHI+8IaZW1g7vYHSj8uGptL132Xn5gvmkshpjBTFQ6ablrVzzW5i7qwWmq5n/og1jCd16rYTAyWdz7mUHoMTTKadep+KCbfLBlsZ6nGFc6l2lyioBeqKLnqiySm8eUiMEXzwhC4SgkJQWmcwY0eIkt4cDRpzMVoDkosca06XNC6tKJejO8EHmpVHWGElpNbcXcidpvplhYhixWVmD7lDUdZFEsEpURNQVZh/SqSqh2zvHDLXGfaqQ+MSYgKhVFLeMBKJ6hNooZqeJUSM0Z5BdI1ZNUCKeFWjisoNWM2X1APHaDhlYB2nJ6dcdpd87e0v8fDhE2JlsKLMLud850++w95kh8rUaOwwajmfL2hix3QywBnHcDBgtQLRwPbuLl957T4n83MGRomhY9EusW7EdLiLq4b4+Zzl6QX17i733/kqL7/2MkfDAfbZCebZU/yzE9yDJ3B1iUaPDmvizjbszQiH++zevosb1Xz84FM+Pf6Q6Dy2HhJiw9Xikp9crhjenmLPGoyGHqS3xVHwgejA1QPqwRCXWSfWmL54ech15TTvrVhSokTW+qZXRumP5DTVor+8b18A4q9DJBvRtQEzsMmh78Cr9ineVtfpTX3AwZDYhv1pqht/is4pn4EQPE+fPeBq/pyoSw639mg6ZVANOe8qjmcPUDGMqhGLywW+eUwjnkUD3fkKG5SjrX2MRE7PltQrz041ZOU7wlJ5/MOn1Ns1dlhxdbIk4Dm4N+bg3gFnz69A5/jGIF54/dXbXDXngDCuh1g3xFrHK/tHfOmtmt/41r/Gf/Hb/4jzT75LNRgy3PFoWBDaGReLwGqu7G29TSNwZ3cXv1K6+YLHxw+pbleM3JDF7IKoypPjp5xcPkEq5c5owrOHp/zge9++1jlMwyz9fJXTsMxViErnu9ScokS7y3wWhw9ybEMY1wNa3zLMTB2r6dyKGfhAyAxXYTAYUR2+RPeXdli8ep/L937C6IOHuPMZVVRstmHIOlDK2lD+VGR1fRYDYggSiZFU7yzbO74Cf2uKf+NleOtN6sNXMIMpmtNJTOdSIXuR3lGwYlJKiiHb14mNqEC0iZVCTJ0vUw26on/pWeXGWpRUL8xg6Lpw/XuR9fcGIj6XNYg+FaBPxdQztCEmAf8GjLO4ymWWqWEwHBJNhZUaYwPWphWREmgKs5a+UKl1Q0Y7t3GjLeZ37zN78zGLB59gHzymOlvgWsXmFFwra95vYQZvBhhTcNcQNemQgBJixA8NerRDfP0V9LXXqQ+PmGztZPZXajxSui06l1OLjKT0mZjt4MISkuyDaAqsSEaZe78jp68YlzjMQSNGEgvWGqV2CjYVJA/X3B1zPZcb9puBwpoOMeYSEYqGkPZc7kxavMCo4H2HrQc4UyWmrYBkkI6cFl1s22TeZfBGNTc8yWknonTzZ7SrhllnOG4GTG5/gb03LJOwYrKcUc1myHKBLhskpMyMUu9P3BAZDdDtMbq9C7sHmOkWUo+QesBgMExnhEvsyxBSuvCaUZHZGhrT2WJNWoOS7GwxDrEWmzNrS+2aVHMwgTxkJgYxpVOl6g8JoErPmNOsrnH2iskcNVIyaKL3Kfi/4WMVP6rMtc2lKYwqw9qxtbvFcdPQ+jYH6pTaCnY4ZfvoDkcv32fv1i3caItv/vI3uJpFHrz/febtcz58dMJrz56ytXOX2HU03ZxPT845m7XUdc1wMGA6GLC9tcXwYAunBmck79FkPwYRpHLUkxGTvR3caIwYh02bjKzG033bbO5rKjMQNXVo7SdSleATg10qm4CWdYGdnA5o+jMFJNUrtNKzwBXNLKy0xoykFKrN4PTPkj8Dv7gYI6xZMPnl/stkrXTXjjobnynvfxGOSM5uYjikQzemqPNoyMHt20yO7qNmhK5adH4OiytkOcdqwEh2+LwH75HOI75LlcODRzVgJBuDYlOOokRM6FLnFBt6kEcXWQme1DDdRra2GGxt43b2GE2mHD95yun5GYtmRdCQDzTJAM4aGFqDOVmBrGEQ0KTwjRZnNb+ckUrJ1L/rT8kwtCQGTMVG0k+ZxgyeiBQnWHvbIZVokRdAD5Mj284aQuf7dCkpbJPy2QyG6MbFSnNtky0lYwzOWVxV4+oKWw+w1QBxDpNp6JQUG5/bEud25N43xLahaz1du8S3Lb4LdJprnfQPaNa2EGsWxrpgcragMoLcdxnq8yLTkxuk/x1AyTDruSJ91LiM+/VOY3G+P0t7Xn9d3l1iiX21V+nR4B7rlZDnaeOeyzNvAFBKKR6agSiVvsNKAsnyQdvfQJ7TrLiqQU09GGLrEcbV63SgbGCnMQwQQwKDVNHOE9olbbOia1b4EHp6qJZB6GdFe6CovFrutTfTNj5UIvtpLvvBzPsg18BKZeCQmIyAEqm6tjksaH1eSCZHDTWmFCO1yciKmow/HwMSDWGVxsfndt1iknGKQvRFh5LYAfME+oQOfJcPGs1RVmuwInRNqpOjXbJgJVFyIKTieAmwUYyLWMBlQzTGdL0EXCZQxJrcEaCuuawSPb1pAoOBTe3GrUHVkoos55lJvkNKbUr5dnl/pbEgFyoPXSr8FkNhSvbLvtdZSh5OTdRi8eBqqEaCrcE4qKvrraMhRtjZ3qESwTee6WSP115+mbdeeYXL7W1++vhDvv29P8arsrM7IUbParZkRYvzhu3hlPnzK9Sm7kqrENnfvcXQVOzdrnjtnX2ePPyEamTZ2d3lwaMHyEvK8+ePeH5xwtH4PvVkiDOOejjl/heOODzcYfTsBP2n30b/5Pt0jx/QzC8IvkVihOjTQBlJ6YDGYQZDdnZ3+cYrr/D1d77A+Ztv8ZP5Qx5UnzBrTpldLBlNd9neG7GKM1qz5NPzBxxOb7E73lqfYSEQSc6gc8nQhrQIJZqUqqcZrCHtgX6f5sns2TWydkJMHwlSTo6f5jSr65HKGV577RaTrTHTrS3aoKxWK+ZXc5pVAuJWIVCnWEfqgBVjSr9TekXfnxAbAaKkB4tNk2ox7I632RrWPLh8zCfHjznY3mPMFmPXYaSjAo6mt3hw+YjpsOZw/y7TwTaTasjB3h6VGSCdx7cpJWAxn9PGyOXlFRdnFzx8/JgHD56wNRTOuhkH+7d4+JMTutWCyVbFxcUKkcCTidIsG+6/eovKOJquoXKG6CLVwZTvffoeKgsW55eYwSV7h/uYOObqfMZrt1/GVRWvH7zJ9nBClIrz5RmhWfHBp+/R+sjBeJfzZ49pusBiueDRw4+5Cid8UI14+vCU5Wp+bXNInoFiR26CFRrT+gkx0gWfzYiU7mszE7fUsrAusRjJDAZbCcZVGBkk5NrYlOIlaw55cfrFOgbjHdzLY+a7R1y8/hR5+CH1h59Qny5wjWJVMaKJUVECGkBKFyWf0SYzM7KDq4rPpkmcOsLtCeH1u5j7b1Ht3WEw2UbsEDEO71NqkHE2O3GC2HRepzEpNlBJVy0dPpN9lQJ2ZTwSyyPGZGulNEDBaDqrqkG9Zhlfo6z9hKQnIM2XD57oPRod4tbzbLPDZCR1XokxEKlSAxIMEZtr+8XeTkqpOcmelbJ28qFSDbYYquFkuGR1f4S7/xpcnlI9eUr97Jxq1uF8QELMbIlN0yAHESUFxoK1xLpC9waYlw6JL93DHt1Bh1OMGzLZ2U0F/DWl+KaDLTMtohLy+jI9HhwJJtlyBcCREAhdwFhJlbM0MVlTiQqLrVKGgYSQnMmsWp1x+OATeGmv09mnH9N1OYHMqjXJ2Y0hAXCqeZ+adP7bnJ4eRLHOZntGQRKQXpKqS7dbKSAN6wBQyVqQbC8CuX5OJIYFy8s5T08aWqlw9Zjx1g77u/scvPQa9SCxlWtbGE2JHaymxrqKg6M9hrXFuRrRSOx87pqU94ZzibmlaT3GGDNAnFOcQmqN7axDjKR6aZrY34Xd0ZcNEMmpuGtQQGNM4E9MPpMCIRaG/HWz+gsbKLPjJRKAJqfHi2pKb8vmd9DUACMoYJMtGUWJNBwebTEcj3DO0XSe50+PcV3D2196iy987asc3nmF4XgbxPErX9nm3u0j/tF/c8CP/vD3OZ095/nJFcPBfmK2eqXrYLEKXM4XCCucs+zsekaTVOjeVRVVbalcDsKGiOkMe84wFGFUV4itMLYiqBJbTwwerRK7Junm1Fho7fdo1qdpjmNQ3MCl+m1iiD6zxgFMqlUkmX2bdkNaqyan2iWTNzW0ybWk0175BV1PPido85kFoeu/ij4hM3EKw6cULy12Ta93eghD2fD0MvABEHFW2DnYZ/f2S4xGB2gH2lwRL88xs3NMu0ztcm26nkTFxAgZuMF7CMkRFFXE5LuXmA3FrGbNi8g1BsQHxK/gokPnl7CcY3Z2GQ6n3Dm6w2Q84dnTR1zM53Tkxtwac6Pc4gCvU4DSZCfnWTNwgFGw6y482vuEa0Tr+iMYYGydCkAVJ0ekZ7hIz6TZAAU0OenlBhOAkQ+/DOQMnEVI6VFJZ9r0HJnqqLmaaHGWC33MGUs9GFBNtxI1tB4mUI10vkoE8V1C4vvIu5Lao6e1YuoKV28h9ZRhXoPRN4RmyWoxZ75csmq7rNjSOlRJCqav2N5vSnoWUG8A5imIeRHHko+4Yav0NQ6yRSBsAjZ67fNYdlGxtYQ1U2aTbWOc641Lzc68qKwPtY3raY5MpHvOr27etxT4Q3A54qfFY84PnabZUFc1w/GEarKFG0zSARvpK+4TA7HtIHpUfaJk61qhUTtkNKIebzEikcFDs6JdzFPB8GaFD4l6/KcacudtnKqyS17OJS2MPA7aqx0tradz2mdJ0SqpYrLBVrpWUXJ0v+BJ2nfWwSSWUfQxdXvSiDjFZM2e0pryfshRgrSnIqWgXyocZzLGmVIhfBvSZzTinKGqDaKBrom0y4BIh7NVBm0TdiOkLABn8gFD0hlRwMfUuSkxRw0OQyWCtcJwUhFWHV3rcSEBuxRHtkjOo0sRjhTRKlCuyWMUY+oIkIrKF6p3ukw5S0Q1pY1rTGNlDZUziAlUQ0c1NpgBuXTOdddCEd64/xbVyqN3UmoU2vLDn3yHjx48Zdl02M7RtCvGk4rdnTEjHXL37m0cjmbe8imRQV3hasd4b4qXQJgOsDrjounYe2kfU6cJWYQrWu149uAxZ7MZw+Wcife8fOc2d3d3GHzyiPCf/gNW3/0O4fyY4FfE6OmjYpo0p8l6ORkPLaFZwuUZ5sFHVN/+59x97XUOf/3XOL37l/jh1UN+2n4EFUR1jG8PWNlj5t0hd+0dYgipjlFvnEdGWxO++rWvchG65CAAMnCMi5HXA+GbwEw2xvN+SH+l9d+0XWpn33X42CWn65qkHg149Ze+QTUY4Kqapgm0vmOrbWmWS5bLJbOwYmKHCJbWe6KuqGVC13VpD4dICIEQUqqvkdQNBZFUW8Yk/dZoYBE9vguMxfHK9ID98T4He3c4m6+4Wsx57dYRb7z8NuZbjjpUVHGAf3yFfe9TzO9/BJdnSLui1NARZ2FQIZMJcX8XvvYG8bemzKvIc7/g4bNH/PHo+8xtw/7tQ/7J736H52fPOXt+zv13DhjtTAlXnmWzwkvHYHFGfNoS9IRFmKFWaDvl8kzxzYpB5Zjs3MY4g9u6w8mV59MnP+Tk/IRPP/mYtjnn/Z0fsbe7zezkiuZyRoxC20WqASzknNPzBctZd21zWOSFQGJSrNnxz45viMTos0OY9b+YXus7l5C5EAPGOcQnADlm+zHmCDw+6Rk1xXGEUpzaVRWj8Q5P3JJw503M3deQ82dMjk8ZnJ5hL5aYlcfEmAJPmaFq8hmoJWXCgq8FJiPi3gC5tYu5e5+wf4sw2GY43WU8mQIm2TeZSZNSY0i2Rw7KpFoY2tN6FEUshBw4NDlokR7DJva3STXVJCo2ZqZbsZGxWJdYcka4XpEEMqwd2QKspQCpzam1IbNzpQLJ4LDklrsmaE7BFVRjArGMoe1CAqoymIpIcuhVc+qmgkms7djMWB4/owuODxdQH77J7n3LdtcyamcM2hWu7ZC2Q2KupSEOrEGrAToeIjtTzM421d4h1GMqUzMYDDCiqWaSmhSv8l0Cg/OzqiZd5+oqB7/oXQtjk24p0fxiT9sqgXTZ3ElthZ3N69NkNgSIGIxN6YCCpo5Nv6BjzZ9VkguRvtPouq9PJI91jKCOkg6vUVin7CtWkvMcQ0gp04YENYqkjieaAgAFFjIbthAiCdjziV1krMmM0JivHyAEwmpFE2bMwgUTc4hUlq7paE2FiFJZQ2gD0RoG011kfxeTi+VLsQ+zM26tzQBYaXiQGGBCAhYlpk6yL7T9Ln5R1JQOrYlDJtkvTUXwU1A0zX9MNlEq1LKuE9NHsP48ZjF5SDYDgcTUjxKT6huWzr+o6RtlGI04Y1BjIcDEGm7dPWT38JDVquPRZErQlt29HRwQ2wVuPMRVjoFzvP3SLoP/wW+iIfLxn/wexydXHO4tsGKJUmPdmPF0wPbeNnfuHXJ4tMPBzpTdQU3VBvzVgjCbwXJBaFtUHLYeUU1HWDzql3jjaWNH55VYiDQqqTC0sYgzWGezzSlEDYjkQgaaHJ4EhK7xA5sN0+AVtYqqT4WkyeB4yOn/Sma0Z16ekPCANqD8fNvmc3ePWntEfYLJ2gbPD0apNbHh8H52LRUHN6WeJGaG5F8YgcGgYv/OLbZvvQbU6NUSmV9hlnNYXWG6FRJ9/kyhFCUkmS6BNeIDEjzELm/UvOiz6jAlNUoE0RJ5NIhzIDnvLkTEr4hXZ7BaIFu7VJNtdsdDBq++xuDpE47Pz2hDyEvb9IBNctzXtCvNLKKNPAEEgw+RmNFmgFIYV3tK+PVKbSuUlsJDKHUpNkEayMh3mb/+UNZ8j2uQQvKBGFRpdWOxaZqbPhmnbAhRxFqG0ynD7X3caJtobMr59AFdrdBVQ1wtsasG0y4TVUA1zWv0qCqh1MQYDhA7QIxFhjUyGGLrCjfYph5uMSHQLK5YnJ8zW63oYuwNpE2wLNcgy+NuXgAAIGQncm2dRE3GBJkmiFH6Asis17z2gM71Sgo0FJq15ENd+3kB0sGuJhmGpEMGq+t9W3IXC1YjJS0uI/ey8SB5/4tErKRoc4mRS6ZkDwdDBtNt6skObjBKDI62I7YNYbWA1QptG1g1aNvkelAJWFWUaAzROGQwxAxqxFpMXaGDIa4eUu1NGO54/HLOfHbFYrHoOyAlYydHDyWiJjum+fFKBJY85309pPLZ3LFNkf53kq2kdc2ja5xJzemhbNxLCGhIEcWuC/gmgTaKYgcgLtFStCTIZ2O8bT3GFn5Vej7vA9aBGEnATxsITchdG8A5R1VVSDCJcroMdMEz3q4ZVJa6rqhdxaCqGDpDbUBKKmfWVZ0PrFYdwafaONE4sBVRDNPtAb5q8GcdMSRWjpFM47Y5cCTrMTXGEDPVPekVMtAviFicqUjFAO06uiuJ9qzEHBlWjAVbG8wgfZepXNLpNqXohPZ6PQxrDaenz3Dq+MYXv8SnH3+MryKLpkO95Ze+/E1ePrzNd//o2yw7D4uK7fE2b7z0RZ4//pT3n31Ah2d3vEs9HKIoy8sTJDS0i4ZlM2dBw8Hubc7GVzRdw4effsgwVHipGO0e8dbbb3F0scD/3f+E2e//Hnr6nBiSU1+eNqmHtVZClTU+sk5r1SgYJ3R+xflv/2NGruZbX/0S917/Bj/1ZzyUj5HtJReLhqZ5hflqRnCR7cl2+q48p8PtCb/+699kmaOq0XtWYcWocr1zuKauS39PSOx1cizgalSOnzzg4fKMV++8gtgVnb++IrYKuaCiJSgpQq9KYiIaogasRqIGnLNUtcOHAUED3rcYE6lHEyqp6doWIdU4af0SEQgyZLlseHZ5zJOTTzna2mZc7zKaHiWW2rimmmzx6s4hr929zTSM4P0T/He/T3j/pzRnT9DFFcQZMa5AfQbqN2wsyXvFWBCH1CN2jw44eOcrvP3Ft/it//HXWE4szxcNs+crfvDJD9jaq7i1f4v3Hz5nZ3vI9GDAqB4xHu3w9PFTojXUlUM7YXm1INYB1FJPR3zy8ftcLeZ894++jwtQ16ZncgnCZTNHVyDR4qoxi/mMNjbs7u4zdEOenl4y95fXNoewPq5SrRN6irux0jMfYkh2WdGd2u+FwhTNNluIOFcTM1iMZFag9xm81ry/koGPpvS/ciYHLLHzrI4fMQvKp7PIaHSX7bdfZks8W37FIDRIt8K0LTaElCpsDMFazGCEHQ2Q7TEy2cZt7RIHE6SaUFUDXAyIq/GaQImuSyEMyd6Hek+0Butcqs8m2daxsk6jMLmkv4DNkX+JCSoJIeb0McHYjAHltDHyeWmsxVbSB3SuTYoCKL5BVEzuOJSsGJN/nQCvEpxJqTEuOY6SUx0wuMzQSMCEZkc5M8Jj6mZYKKhCCopYk5z8qC2r1YyrJ5e0Ty1PnMNVA6bb2xwe3KHaMrSNx2bgTkk1V5yt2Nnd5uVXbqeztKrpQqQNkc6ntJfKSL9mVbPjLim9whoLNlI5i81AQAI6UlAjsWLt2g8zxec0OZU5s01DAOfSc4Vsh+ezt3SIrFy1BsuvawrJQDzkrrQpMOcyONQFTcCLKfOd11WxoaP0rcLFGLx6UItKtiOiJjAnZjYyyWovwZ7eB811QyCxj1xVMZwMOdKKejrh9uER+1vb7NRjplRUq4AsGuhaZNVg2kgbI0EFWSrukycwnhK2xjAZECpDh6a0pdKAoRyzBRQstmNey+uCxtnnE4MtaXqSFE3I4xCigvrcgSgHmCUH7YovK6mYc/7Sa57HFMyPqrh6AEDjUy2hvAsT0634tAUY1FSLTVxNJBB9xKKpPpSmwN7ueIuuCcwurxBbI+KY7hiqeoyI4eWjKb/1W7/M6uKSs9knzFaevd0t9g73ufOlIbtbE3bF4J6fEt//hPjkmO75OavZgjpGxhKxeHxtWcoI3wqqnmAUM6yodibUd47Ye+0eeveQ5WBMQ5WwQFPnu12fKcWP6gPFGUg2mzolnxUpZdKs/XqUwuhMPndapKXMdcIwfErN4uezwT8faFN08wYQU3zYdemQwqBJjqT2KJ0UL3P9sBuAj/TGhzAcOm7ff43R9i3UC7KYE8+OMc0Mokd8m5z4mFOPSgSACJmyiO/A52hc6JITJ6RiaprQMUIq1GBMSpeKmjad+PI8GQUT0KDQLVP6lO+Q8ZThZIu7917CuopHz57TFkeXmGt8rAdn7esVsGuT8pk2wFr9lk8mNXTdUucaBWqLG1480vVCU9PfMJuFfNe09fKRND7WJjS1ze36iOtUmeIQq1Eq6xhNpwx2DrBugnpFVwHtFmizRBdLWFwiqyVV8EjbYLpV2jS5jgnqU7t3m3KxWbkEIvkEugVjiIMBZrqNTMfY0YjxaJvRZJvp/JLL83PmyxU+rsELzc/SixQHnuREUNKKMgNHSuvSNftMsxOOSGZckffDn4/0ubLFU4L1miM9j60cIW9aU9q698oC+rZ9GdktEYtiRGyom/6qkBRYzGvFuop6OGG0vU81nCTbp+tolxewWsJyQVwt0cUV0nbQdWi7woRA4ekm7CRFLg0CtkqRxhAI1iHVEDcaYbamyHTCYDiivrXFdLlgdnHOYrGg8z457yTgTXLLdc23vS4JqBuKssCiGyAWOZUhs60K6EgGEK5TYlbwZWwlG1Kz4yXBlzQgSVXdEDAh5dZaSQCOzQ8QIlhLDBHfpQK+xkAwgq0E7RTtInSaptpBbCNmZKlriwmGOnomdcWr+zvsT0fsTrfZ2dphe2ubyWSLSTVCQkrpTOo9gSVdaGiWSy4vZ1zNFlwtl1z4JScaGQyE5wvBd6BeCCatJxFyVyn6QtBYSd0lkJyLLqikejjGpHoUy8UygU7ZkTICtZUE9ljFVGCGYIaRVdcSloEwU1w0uGEyqPzi+hgaAJ3vePD4AUf7t+lQpKqYTi1b0wntQplMJhhjmIwmnJ8/Y7lYUb2yxfH8lJaGplugBnzn2ZlWDMc1Mba0bUu7jMzOL7FDz2pxRaORN1/9MqPpNtbD27cO+OYXvkD9+3/M6j/7+4QH7xP8AtC+LkefZqSF9apQOYJYZDBEF1dr9qMqGEcz2eL89Ax7ckFAMQ8+5u7Lr7D/V3+TH23f5w8++OfMY8fidkPbLelUmepWopMrCcSVZCDX0eAlsIiXzPQc8UJVH+JIhTW9b3LahaGyFSbm9A0RVm2DMxWBAFXFrdFtRgNHGNTXnOaW9FoMoXfANSamQde1KXfd5VacYgvUlIxwSRHSqAGkwlapzXUFCUgV5Wo1p/MtVxdP8M0ZujVlPj9ntFuxt73DrVu3megUffcZ8ds/oPnh99GTj8Gfo7EjnSiZcacp0pdAtlIXqEfds90VMSywMmD+wbu03/0uplPql+9x/5tf43/+1/8N3r/6FXTq+PDhCU+P/ynnl1dMd0e0bcujx48xfslou2K5FC6eLwk+wFbHndeOmNZTbANXzSltcw6uotIR050BUOFMTRNWzJeniLOMRjXDoWMgHbFacN4EVrHBja/Z2S9jUOY0g4MxszxjUDq/Tples1JlDfDklIRifVU2OUsYA06IhRy00RFLNebMTi1xASyJSRxjxC/mXD4943kU1FYMRiP2t6ccHh6hQyF2qbh/bdN+7aJinGN3Z8Irr7yErRJN0PuQOrJg1nUQ8lrVDNakdCGbHGYrOJccfmLqrtc/mbU9KJIcvuJwpfUdNUIAK6l7YAzZ6RTTgyTOmuvv4pam4wU/I9N08V2Xi7sqUtnMNignedJxzlZYI4lhb1PqgjWSC58aNPp8/ktfM1EzWJPiNqn+kIrDmgTiBu2wBEwI2NgwlRW7MXJoK3Z2phhGVMam1FMMoQVjK6qdEUNbIVKlyHvQ7B5E1JkUlHKJnYGk+nBRY0qlAcSmNsHOJfBCcwqxQup2pakOkeTD1PuANZlBLuQadJ5KcvA0FgZ5toU0YsRROXftuNuL85h8iJADBQlHianOR0451QxmpC5EqeqQMak7ZCJ3eMRUuT126thTPpdaLidApKTd97Z9fw+KwTHdOWD//g5f3jpkpDWj55fUj57ijj9C55d0V0tC55HQYUKbVsp4Cw0RGzWnNg0woylxug2HR8idI8LRPt3+gCBKkNRsIWY2dDotCstJeoe5FEuWPD4mr+EYEpsmj1y2tdN+VFIR6VJaI/kc5bzf8LGvaQKLe6ExYkVzq/IUYJcq+bwqORUt+0Y2p4khFhNTHdXkb2Q721i8wqLtME2Hmy3BXjIY1oxGI5wbIk6ojPD2/UPO/tLXePSBY3p7zMv3DtjBYp+don/0HubTx7iLc2y3IvqWynuUvCZU8RoJwyEystgoON/hiJi2xVwtkMcn8IP3iGPH4GCL4Rv38S+/RLtzgIy3iTpMQGgG3TYgjMwwymfJRpZCP6t5U2lM6zrGDNoISUdRgsoJZEcoHPOfK58/PWpjQSRHer0ItQAdaJ8KlQAl2dy7yTljDQz2VxRlNHTcef11htN76HKFubrEXF5AcwWa0pz6tCffppEzpt+cCXgBX1t0Z4yMJ+hoglYVal2KmihI6JC2RRZzZL7ALJfYZgXB5+JWoT/tjVjiapnv3adIUrME76lHI27t76FReXJ8QhN94tj0z6b9pi1U3R6wIRU2DCH0aQf9L0pU5PNN0L/EDEI1GNDM10klfVZ1wikSY4QCCuRNWxZoTjFJzKjCXlIGtaG2JhUizWukVBY3kiIcg60thruHGDtC20C4mqPLJbQLJDSYdoU2HTRN6vYVfJrjkFtoal40kvN5Y8jFwgJIRBoPZaPMlXh5htY1Mpxgd/dga4vxYML49pjF/IqLy0tmqxW+AFIKpTNY+TooqU85KoPJ6VUZ0ZcC9iSjIauwHgMAeLEl+XVO5trgXM+u9C8JKaqRWnJbom6whfJaLIWDe3rjxqp4kR8kObUiT0NlsGIYTrfYObjNYLRF7ALhckZcztHlAnyHiR2xXUHTYpoWCR0aUr0pcu2aFFhIBeYImmtuZMu48dkIv8SfgxxXKRd8exu7t0s9nbB/dMRkuWB2ecl8taTN6yXpmwy8Fk2an9AUQ2ITvOknfX3wFbBORT4zHv/fS1C4alOdmBKls0KiTxYjwyRwww0sdlIRNOYUxhRWC8uIth47dFT1AK+arlcZbCXU42QMBl8AqGyck2vPGNjbnrL30pTD8Q53dw64vXuEaytWz2esHl2wOn7M+dPvEc/OMU3TO/6pe5zBjCqq3S1G+/vcOdzhtaO7VAdT5tLy7OqCB9PnPDo+5eTqistmxUrSBMQYs6NLSrNwlmAgdkmZS94zMc+JrQYYW6Gx3Uh9E7BgajBjRYYxFUgWwS/TGooxorNImKX6BtcOvMUIVqkHhsvzY0QC73/8U3y0PHt2ztPLGa/eusP2nVtUo4rHl2dsTSZcXJzw4OEneK/sjLa4tbOLc4pvV1Suopl17B/eZjSo2T3cY2u75f2PvsvVRcev/ea/yte/+HVuWUv4v/19/G//NuHqmKipnXbPwINcj0nAuRTdix40gWBxNEbFUl2d02fPu4rFYoG7vMLFdE6E1iMffIB5fsxXf+s3qF/6DX7QfsSqveCD4/d54+Wvpd0UU3rasl3iqCksBmLk/OoZH168h3HvszU6YCRDusZzubhke7pP1yyZ7h5SR8syLhm4ActmztbWhE5mrOISf9kwD7tEVgS5PvBNkBTRLZT2guHGktacCmLGmFJaYkwgTTkrSmvWFO0Wgu8QUbwGxDgu2hWVFXb2bmUD3fDK0RGvvPw6ozAh/M7HrP7JHyIf/QBZPkVZoZrrtiEbwehiGKYuQGKEkkKvOa1XS0226YQLb1g9eUi1uEpgwMkn+B/8Ee7oNu988xvor/8y41d2efzhh3z7o+9xtVwx3R0zzNjutB6zDCsO9gacnrc8+fiSSvawRxNu3dnj/sTRtfNc4Bfc0DIYVGgwdJcdu/sjlsslF5fHDCZjnDPMThvmV2csVwv4BRHFP6uk2YqbpFgKa1JUCW2TQDhJDobRdOYY5/A+qVbjHKFTJHqMGxA0UuVId1ToNKfSGtvPf6mhU1pF2wyYKBDVYwhYlKG27LFg3664Vyvj8S7WJIDARQPiaHyHqSzVoGKARdWmgtjZniSm1B9jbE7JSEddyOCDiE0ptgCS6P4xF1VNvnuADsRKXysj+JBqM0KfchML9R/S+hZNEfT8Umo7XF/vySjp+wsQGbPdZ0wu0Et6npJBUHzgEFLxXouhMgYbU2fWEFMAwxjS2doHqOiboSSDLYFApU6IqGDdgNo53HjAS4cw2d3h3stH3N45YBxGmHkknixpjue45YLh8gIJkQVCUMEOBlRbT9CdHdjfQQ/G+HFFVwrKlsCjJBvNZMDlhZTvDLYZ6EEmhFRPx67T6VJBX8WHkGtspPWtKgSf22PnAqlr/yIVlrXW/LmYqAU8KaZVjDGvUcFl577ot/KsIq7Xo2ITY67pOqJxmCipiYGxOLGATylzkIGePu6WGZoxlzEStB5w+9W3GVdDqosFfPcDzIc/wV2eoKuGrutQDXhkzZ5QUFsRcz0abQM+63y5OEGsQR44XGWphwdUL71NfOd12ru7+KlNtY0071VrSVWvEztPxKzBjlw2wBpHSY0zkhh0ZW5DKOZsDq4IuTZksndijLnT2/VOoULiQ2hhxYJNGW3JdhNJSlNieqbi2KflRpfThkuXPRGorEXUMF/M2Nqd0rQtXdOwvLpKHQyHY4iCxTFy8EtfucPX7lrGF6cMf/wI++FDzNNnyOwSuiUaWrpcczOR0Nedp1WVsFohASpTZVtfibkerXqDUOMi6OlD5JPn2N33GN2/C6+/gr97F909oFOb7NJSj1PBR8n61fbf1cfSC2CT8TaluBVpcaY6YRnIzf5VYvv+4o34+UAbfeGvnuLcd8nJr6/dPe2dpyjraEQRUzqDZK07mo65c/91hpMj4uUCOT3GLM4R3yAhGTCJ998lcMV3KV3GCnE4wI9GhOmEMN5FB2N8NSSKXSNk0uPLlCKkHKaSmDZ02KsLZHGFvTjHXl1guzYV2jWSEFUxuNUKaZvU8SkEWA0ZjLa4tbuH95Gnpyd0ZOCGgr/kYsy9Ib3m1FjzYiHiFHhPu0HIuc7X7CwO6prVJRmN32SCyJo5JYAYJGxwLPqiROnvSHF0FSMWawxt26zBuXydejxhvHOEG20nNsz5JXJxCavLVAiakAKEwWPaAF0Hoc1sqIAEfWFsSltu1YRKJoc8FRnTjOJK8Ejs0EWLLuaExQwZTzE7u9jphGk9YnA0Ynh1ycXVJSsfcvenDLQVoIYU1QzQI/Za0PsyLrlGRHHre8S5h+eg7x17jaJ5fBOQqusXSpSB1HWtZ79lY6CfY9LeLR3AoICvJZP8T39hoeHeunuLycFd9u68gjOObrbAnz5Hrs6Rru0poBGfcm47v96z3qe5LTWfEERDVigleilJ0+WDKdFlA3QN2jTE5Qy9ukB3t3E7uwyHI6q9ferlkovLc5ZNR6CAx7K+97ww132jclUcTeCclPXdv0f6+7xum0ZVCZ1PxmQ+CEprVtJL2Mpihw4zqZBRTWw6TM4bjk0kLj3kQ7EVnyo9OYMdGKhSDrhGR7tq6LqAMcpgCEOBt+/f5a3XXuNgtM/YD7DnHv/TM5588Hvo2Rm2XWJih9HARLsMpCWDqwdyI4gXmD1FHxkW9YD5cIwfT5GtLaa39vnqwS3efvsO52HFk8tzPnz8kGfLKxpNzkcyyJORlOY5JtozbLRHjCyWc6ajIa4SXGURJ0QT8c4jVZonCekwjkFzO3DBTV1iB0XW0ZBrFBFhMphycn7CfH7B4c4hk3qXdz/6GN9FptsVaiwXV+eoKl95+x3abkXTdBw/OydG4ehwlzdfeZPjJw95OjujHabOTPP5U6ZbQ6qgzM4u8Cy5+8oXePWV17gdI91/+B/jf/+fEFYX6V7SwkqguiQavURFXcVyZx8TIvViSXQ1wVWE6PA727hVg+uWKZVuNEbms5R+Ab0VrES4vCL+4/8Xb37rm+z9xjd5XLe44YDz+RNoGqaDEU+f/pRPrk748ivfIkRPUM/57BlPz45ZLg3VyHK2XPDUn2MEKnVcXa2Iq5YnJz/Gu9TlxRSa8fkSNS0iBt+0uPoR1rW0fnWt8xiDx+D6bkApMuZxuYV713Q5jZMU2c7smuhbDAENHdhc3l/gZHbF5fw5EDg9OWNne5fYLbl9dMjR9gH3b71J+M5z2v/sv0A+/i6mOwZtM1dQQGKq8cNaFxJTarEOx4QQidtTzMUlplnRd+kjIpWjFUf35JS6WSApRJsYx02Ahx+hTx7A7/0ur/zl3+Df+Rv/Jl955U1+9Pj7nIY5tR3TLQKLVUs3X9CtApUbUVeey9NToKOTC24f7lO5MfPzxMQJPnL86SnT3Ql1XTNfLnFuTOwWXJ2vqIcTjrYPmMqYy7P5uobadctnbFVyACOxYEMCOaBPxRDIdlh2KnJwKGUmxLVNJqldcyjphDEVD5GN7xVrMgsq1X6rnWN3UvPO3Qm722OOjg7YGe0yiDWDlSOeBeKswa1WVPMLYgzMjUGjpXYD7OgC2ZqgexPCzph2OiAQCEPbt5sFRUsHtlwLRrONVFJpjZTU4UTPL+1oY04X1xjyEVzOvRy8CwGR3PGxnJ/ZkxPo6zVc79xtss2Tj5CYtxtsoZyOUT5kRYhEKispii2kdHtnEggn6XxtQyT63LZXSMBd3mL0gEaCTYx1HBzcYro15p3BNoOF4h6d4/7op9izE+JyRrNaptpERHxsUevoxlvENiIxEsRixCFSYyfbDHZ30VtHhJdvEW5v47eFdpAYUqnGS8x2gH4mlSnXazO5864IkNovh5DOZaPFdk1nawypE5jmTk1IKvCrGrG2QjQxqSprsX8ee1GL41xsqJjrFuZUoFJP1BTbOddzUfqOuiH4nIpfaMWSgjeSaheVwEBS23ld6NouLoHmyjvGz1dUP/4BPPiQcHmCb1eprlCMyQbt/RrNKbtC7DrMfIaxdV/LBlWCeroodEawfs7g6pLq4jnmwx9S3XsVeec1uvu30OkAYk6DSchb+h7Ndc8MPaumrEfUZPBQCXHVgx1JZwWMrbDG9AWYxdg+jey6DdU+W8aHBJRJamVuEFxfMTynX1JqxSb9GVRpvc9NA2wKZhhS0E49y8UC3yww4wltpzRN4PLiEjscIG6MFYNZtUwfPsH99CPshx8hx89hNSe2Teoi2dulFEL8Z2x9SQ2JtEFsBOPWQLUKjQrRg4sB03jqCK69hJM5+v6nVPdvEd96A3n1VbrxDlFdbm1u0hyQAycZFNUMyNgNlSi56QddSIFK1b5rXdRM2sygP8oLfsBn5c/UPSptuOLg5EF6wYkt/2ON7EAPEMjGrwqTox7WHNx7lXpyhC4aOHmGXDzHqE/ghW9SVEMksWAIoAFfGWR/D793m3Y0QauaQEWCYuIGMyg5resyLZq960Q8jmaI3xnA9hF2b4m7OsedP8WdnWB8wCxXyRDOlCaJHu06xLfYpmMw7rg9meCbhuP5FV2/aDash8xmiP3qiglw6hIoYNeVa5EXDqTrdfhdXRM2S5YUxSplwRfjX5KR1zu0WiaXAuUkxz9FGI0zLFarDNpErDGM9g4Zbd9BoyXMFnBxBmfHmCa1YS/7W8lFpAuDKgNymtPgBM35ohkdlYReq5i0YTTdk80QsBqX/X6fDzCPXJ0RmwVxvoPdmmCnE3Z3dxmPxzx/dsKsbVL+KLpmC6G9Ea05N7Vf9P0hmVPoCnhZGAKpcjF9JOfaJd1HMtQ+uwHzmFqTgadyy9LPYZnoTbAJzJrGyBqA0jL1Yhht7fLVN79O5UbQtbSXz4gXp3B1ge26ZHjYjBxrzrHvPHQdsevAh1RYOhdJTMZGunZ0Liuu2N+nlDZ42VAT9RAMcTGHdgXzBbK9jd3dYzqZMBwOODk54WqxIHx27LWUuS15qeWwJ6ccFMBrDXz1+/HPQVKQQrCVpRo4bGUhRKw1GGdSCpQRQgQ/S4BoMcwJMQHfmplDXaZNS4oumeiRVgmrQGwClcBk7Lh9a8pX3n6HO1v3cMcd8vEV7dNP0ZMz5PKCankJYZlrhq33fLb8c5vOvM5KWEEhJdILsVHCcgXHx8SHD7BbW5j9HXb2tpjsbnPnS3t8evmc9z55wEWzTPnFRlH1RK8ZrM6Xzvnr1glRVzRhkRxl53FDAZOiy5Ua8AIhmQ2+pFDlwy9KOqutESpzvdF9AS7PLgBldGuHjx58iHGJcXBwMOb1128RF8KDZyeYgeObL3+Tj378Iz75+FNu79/h3u3bjEcjOt8ilWU6nbKMniZ06LxhXA2JIpyeLdm6d5tf+9V/hS8Op3T//v8F/+3foWsuQXP6IwAphYFqCMYQ20A33Ka99wb6+qss9rbR7S20rlOq4qpFHzzEvvcTquNnqG8RH9cGcE8pzEBs1+L/8A8ZBc9r/+a/ztO24eMH7/HDOOO126/ykw/+mJde/zpXi2OOL445PnnC8+Upq26FlYr51Yqt7S2G1Ziz02dYWdChNM2Kzi/xPhL8CjeYMLADYqtYO2Awtgxqw/GzE7yLeB9/7px8XlESyGWsydFNMssAbFVhmgpkmWpKdB1CbmPuA66qiDE7UtnxObm84PzsISYumYy2cbu7KML+wV3u377P6Lmh+T/8Y+wf/lPs6hGR5QsnhEAyDIFohOgGGA2Y3DVRu1S8vVOBvduMjp9g4yqdVFWFbm3BsqVuVmuHaSP/IaomEPbsOd0/+Pv4d3/EN/6t/yFf/Obf5ofvfo9FvGRn+4j3P3qPg4O3kGXNh88+hS81TOoxi0XADGrGky2Gdpvl5Y9p7ZL2aoE2lqtnS1xtEmgXW8SNqAZCZWG+XHB2OmM0rnun49pEisGebZiiRzSnvCAEn+y+dLYlp47sSKTkIMFZQ5edDtWQHaZIBTSRlIafD02B3lmS3p5J54tzwv7hlOlkl8ptMVo6Bs9WmHfPk/Mxv6Brl2jXEOlow4rghDjeIzbgg0/tnk2FcTVuOGa0u0c42Ke5d4/48gFxZ0KsLCVVWyADtiSmAcU5iPQMi+wBau4rXMao1ORLeFRxwAKps1U6E43EBBxmQPEXORh/Vukt4WxXRb+2h1UDQUNKedL1PIsRDKnDEJUh+JhteyVV0zDY2qFtZgxlwKCH3Ay5K2LuXmktu3deorrao3p4hrz/HvHxJ3B5SvRLfPREIj6mlDVKcNY61FTYCBIDgVjgLUJ7isxq3Mfgvr+L2z/CvP4q8Y2XiLe3iDY9tMm1aqIthnqaL1sYSDEi1vWsC2sk1zhJQTgT0vtCz5QSTG6XjWSwQMDnrmplDK9bej9G6dedy80UfAyUphCpkBhgDKohmxwx2a0xpHVsIiF6vCb4xocOH3xf+D2lnRUwK4NVIklvfvIY970fYD9+nzC/IHYdIQaCTzWCTNkErNOUyvlnoke6dD+4IRiLBqEjsiDtK9v5zExrsbNnmPeOsQ9/invpTeJXv0Tz6iFhILnjWfYlJDn7IpoLKKduoUldCyo5aGGT3jIF9DIOa13f1jztl5jKR/w52KnpbEwki1oSQyYWtl22AWNMDF+J6b4L+83k4EdpfZaeMaAYWu/pouHycs7O7m4CH1EChtWqZTRymNMr+NEH2Pc+wD57gswv0K7tW6fnlnDFjdz0SpNuktK4IqfOaUBtAlw8wkqFxjicGkLXJpabT3oXPKbpkIsF9sEzzJufIl9+B3/nPtGOU72v/F+v+PP66zu9FSdb0to3uQQAWggPKWBpXQa+JXke8gvOxc8N2mScI+vP5PqwcW/9mOXF31PV8m9FtQdtIumwrIc1By+/wnj7Fiw6OH6GPXuKrK7WYwEklkUuLGwtfvcIv3+LMNkh2AGB2DsTSiSWFoqUuh35OM8PoeW3GZQoijEOJvjBELt3gDs4wx0/pjo+xnQN1IO+0rzxLcRAvJoRry6pj+5w5/YR4blwfHWRmBs9C2NNEpQCBGQjMTEdQoqU5JEho+jX3vIbsPUgHTIbAERhkaydWVlXac9Of5RS6yPvSJTEbjFYUSbOslq2GBGqasj06A52tAuLFmYXcHGMLC6RdpUAmdxCEslOsUbo2uTg+1SNXzufmS2aDc/SQUoSg6PfrgIhZPTeYEJa/KHrEjC2WiZF00XoPN3yErvYwUwnDEYjbt86wJ2dcz6b5cJfBYBJ84CuWWMlZ71EY5KUF+nfX5ICS57un4/kdbyBD5ZbgdRmMMYedsrP00Mga5BJ0+ER+5oumhXLOrXRVI7Jzj67t+6jHvzFKeH8hDA/x7Qt0nWptor6fEAbJCaGjXYhOxqBKJoK0FVVKqSX8+/7iEkIuQhBQAKg7dogzbn7Jhc2006JXQOLOXG+xO1uMxiPuHWwS2UMZ1dzPJG+c1Z+9hJRjZndI6T5jWiqS1LGNL+3RE6vW6phhctK29YWO3B99DlCos2G3PI1hJz/bnpM3LqUr+9qQ5C1MS6SKM8FoKgUdqcTXn3lFm+/9Sp77GLfn6EPnqPPT9CLC5hfIWGFJR1Ym+5PMepKSpSScngleIrlnEDSkMBKnwog6lwJ8yv06gI528Lu7bB1a583jo4YvjHg4elzTpdXzEOLlwTYOGeQAJ1PbBnjDK4yBO3oQkPwAR9aQgwQhXqYqPMiEGMCMI1YxLKO4JnEzqlri73mOmHJYYvUgzE7w31WswY3sty+tU89Fp6efErwju29CdOdbdqu5enlJVJX3L//CpPhhIk1PDs5QTXQNksuTy9Z+SXVxBIbh3GW+7ff4ovf/FW+dnAX/x/8x4Q//D18O8u6BoiSjzCbWDT7t+mOXsK/dI/wlbeJL92Buv4ZpD+h+8oXCX/jr9A+PyH+yQ9x3/1jhs8fY7pFNo7ytUtNlSbAD96FwZi3f+PXcLuv8b0Hf8LDp4+5isLZ+TPOVg95/Pwxl+2K0WhKs2gR45lfzTk/P2c8HqX6TKHj6bPHGDdGNTIYDOjmK7RaMRpuE32gmgzwXeDy9IQmRMZb41TS7rrmEPp9HmLqbNK1LT4biF3bQlSC70C6dT2R3L2lqgYJ0CSwUJgvZ0ynU6bjO7Q+UIWO3eGUu7t3kH/2EP///G3c0x+AzhBihsr7OwHy7lMIozHL6RZVSEBLHI/wBweEwZDoanQ0Jh7fxjx7gJkvMSEQtca2bd63sr5y8U9zAEkVaBvk/feY/Uf/V9xv/RV+81tf52wID8+OMZMJu/fvM5EjZHubo8Mttndv03rP6ekJs9UFe+M9huOORlfsVod89zvv8/jkGL/yqLWpVsvE0rZLzo7PsWKZDoasWoc19fVNYhnBwliQcv6CL6k9Jo+s+mKRIkExNhc3dbk1r7V0mhgMmKTrrHHUNnXySbXGNDkArIv5ljWUTRR2jvZwowHDxwvMh4+RR58g58doc0kIDSF4gpQUy+xsdmCosNEhpG5kASE0gl+cUC+fYT/12He3qfbvoK++RvfmffytHXyV9KXJNXgSoFRsWwOS0lKtsdmWS051AqeSnWJysVtiRG1uYyw2+95J1/bWjNDX1bnWOcQQCb3dVbqxxZ59K33KlIYCDKQ/xlpS/C+k5izBgZgUwBIhxJTCLzHmot30HVyjJoBFrMFctYx/9DH2J++hxw8Jq0u6dkn0PgUwY264kjdSSrUD9Z56OUNsTWEOR6CL0BgFPFW7YqAtrjvHPPsY9+MjePUN4lfeoLu9RStd33Gu9wFUkr1U6u/kNScF5IlK23b0gZY8VlWdi6kKa0CoZ6QEYkj1OoJeo0KluM3SBwxLkJ+cQqM5zVszqyidMxHIjLHoMYDDEm1ieEQM3ndU1idGbum2Cb3N3bPJDdj5DPed78KPfoyeHtN2TQIq8wouBfDL+GgGO032c0RINSwIqf6mCloNCcYwC7B0hpGNSJcDWV1DFJt8g67FzK8wTx4w+MKXid/8It2tCS2B0oVBig9RWBcxpE5upcC0Jt1VOP9FaZcOmbAmEqR6Z9cPoFLsZgF1mXhgcvaMZZ0GlNn1NqeQoqlO18A4Okk2rEoC3rrO4kNKL22DcHE549btLXyb2taPnKH66BH2Oz/CfvgBXJ6hbemCWbiReY1l8LiogIhFJRfjF8e6HATJeYtCDDWtMcyNEkcD7LCClSALn1MoY68X7dJjuwCz95Fn59hvnBG+8GXawVbS9Sb5vwmwWavCUsMJTX4+GWQO0KcvFtutAD/lvP5F2vRzd4/qa5qUA+qF4UszrP3/17iA2fx3jgcIIFbYvX2Xyd7L0Cpyfoa5OMU2S2hbqAy4XIAr+lREbDTA37pHt32LkAuWksaMdOzpuv5G2ZQZ0S2GUJ/+URxTSYtMcvRfEdQOiLu38dNdwvgRg0/fp+pa1KZiWMlqjAgBfAPdkoFOuLu/hw8dp7NFbxhtpoWVtJkEWtl0UCikLljZpS5G1YYRd10ycMmQKgZN7++L9ihvUijZcSwKrF9gGYTQ/DSS6ltUVcWqbRiMx0wP7mLsmHi1RE+fo1fn4FdI16XUGJ9r35jQ1wuR3AIwihKrCgYVWtf44RDqAerq5OxDQnSj7+vemK7FzOaY0MIq1cGRWDaBgfEEFQfNAroVJniIgbC4xGxtU00mHE6n+Lbjqlmlso9lfW9QkETItLYNILLMq67HMveWSEvsz8Hh7+NDGUHq6yWtNySQ6L2xEJqEnEeZ557i5MsL6zPd9Pr5FKGqK7aO7jLZOkDnDeHslHh5As0M26c8xZTaFkNPxVVJbDIdDgjTCTqeosMx6mpiNQBjUztPSPGoGFPaoW+R1RLmS0KzQGazPu3KAFI5ZDBKxatDgNUipQmt5jCdYne22R+P8K3nfLXqu0sZ3Uhayye7EPPhuS6OHlFs3o9oBlmv+UAUEepxhbOSiwmaNFY5ZTK0pYhd2qcx5KioLRszGa+pU5LNKVZCtAo21+JQocawtzPitbt3efv+q9wdHnH1vSc0f/Ie9vw5tp1hYovgKSsXEhgb+0gFiDNEZ6GqyZxezGqF6TpSfna6b41h3coWTZTQixU6u0RPT9GTc6bmDd586Q5bkzEPL054fHHG2WpOoEvRT5uo7jEqzllcbRGTxiLEgA8hF1tOxQqMJbX0LHW3rOBjJHQe32mq3SCSCzteH0MDUjHOrcmUs7NLfjJ7H1dbTKzYGlvmz5ecLpZMxtts7+9y+vwZ//X7H3G5nPPSy3eIVWTvYJdu2XJ46xbLi+ecn5+jatje2uftN17FGUFNzVe/9Zt846236f7P/znd7/w3xNVFnq8IUqAoRW1Nd/s1lt/6ZcKvfJ24t51ypdOqWxswm5CzEWJdw0t34KU7+N/8Va6+/yPMt7/N6JOPcM28N4qLnqtWDfVP3uPg4cdM/vJf5rnZ49sfvcvgYItPPn6fVbfCuAFGa65OTmkiHOzdZjKx+LBgeXnJ1WyBSGrzPpAlnW9pmit801EPB0i3YrFa4RZz1DfML1d0UbFuCyvXVwBVScZxt2rovHI1mzOfXxGDp64M7WpFu0rMvar22HpISc7VmGqVWFMzbxve/fBHbBmYm8DZ6oTd0Tav33qdI3NA83d+B/n9fwDLh6i2uYAmfaZHchKyoRkDvq5ZHB7RfemLrN54jXh4SJxMe91ZDpwuxNQC/GqOnF9gHzzEvPdT6idPcbMrbNcl+n/WfTkYTUpNVkwXGT1/zvB3foftH/4x47/1t7CvvsrJ+BnHzy65cA3nqzmzi0A8PcM3M145uM3F/JST5Rl3d26xOH/GmekwkyFymtaaqGW4A1IvmD89JbTCG2/eYWe4y/ff/RA3GlzbHCaRdYBJin2f2jeXdKAuo32l5kcfhUHQkGqioJJrvKRrKqQi2cZlZkUOEKW+0f1OKtH9KIqsYOvDOfW7PyU+fA8/P8b7BcE3mJ6lWHZltoijYhF0NQM7QIxDMpuwiYq3Lhn97ZI6drjVDPvkIcMfvUt8822ar7+Jv7VFdKVrSbYjczektX1QFlui+IvRVKcntxoXSXpNcqe3BAin8UqBoNLJqefwXrPkcdE0o+molr6eT+r6RLbJTA4Q5xQSt74/zQWIJT928AHf+symoW8PrSadK8UWkh8/ov6j7yGP38evLvCh6+tWSQEYSuaA5A4+FKsqIqFN559LnRSjWuYa8WKo2wY01cUKXUp/Mc8WyMlTzCefUn31q5ivvkycFqCFdfFXTUGCVO8kpT+RARAh1dtIxYmFEHPNGLuuw1hqFanm2iT5/TnWc+1iJK2tFKPOTAtCKrZMDq6aZF/1zCfJQJyxGCcpYOOhI82rkhiz1qQUqpgNc0GSbY6mZ3v0mPr3fgc+/CntoskAD2zkGfUWbglQa07Fj5KcceNKBkLe66pE8XRSE52j2pqkgrldxIWOvjapJPs/tCviySPMH13iTk7g176Ff2Ubqjyx+a/sSvWOe9JdoU8fK40XNINUxlqMJHZVqTGlmjp0/nnsRkhgQySRDKwkULhUhEJM8tsMlAZB1qYUL2cdQSOteDypfmXMwdmRq3FSpdKlnWfv/j6HO7vYH3xI/Qffwz7+hLi6QtXnYGbsvw+yPsKQuorVRFfTugHteIJub8N0CuMBahODnSaVzIizBt+0iFXGQ0vVrKjaBiMOxOdadgrqKexMuYqY5inm7Ap7foH5lW8xH+xgYpX3I6AJe1cET+qo2mes5fWvqWASkEGcmOs6iiV4j6vdL1Spn7t7VP4qNvOhUspShpikrO0MhFCi9/T/TlFag4iyvbfD3q37qDdweYq5OsWs5kiX2lqqGaDY5FQZodvepbvzEn64A+J6x7pApT2GWg4mlf6vBKCUUyuuNeKGmw3rgwIVokRiNUDvvkIcT4gPP6C+uCAG329+sYaqqgihg67BVEPu7h+w6gLztknfKfEz9wqiFkOOCvTMDCgFfAtL59qlr8pWcuelDw1Jcfy151BBBiA2R6owMcBSihKPhwO29naY3r6P6Qzx9BQ9P0GuzhK4QsT4hGTGrkmbqNgOzhIqh+5s4Sfb+HqEDkaorfq27FrmUtMBYEwyxgz0zr6EFju7xF6ewul5SmsLAVm1EJd93UONqSU8TUNYrNB6SLW3w+3pGOssZ4tFVhJ5/KVwkDaMrLyuCwCXKMmaDQnpgbf0hdc7hS9cTn+Gos5pPsZYOklGrM2ngxbkicxeKeAbQhRSO7o81iKpmN720T0m033i2SX++Ck6u8jd1LIzEFLXNvWe6NsEtI4m6PYU3dqByRTvKoKxCTwra10Vnx8oRV4k0z5JxhkREzz28hw5P0PPTjHLWWJktT7bnSldyIQOnXf4+QLOr7B7uxxsT1ErXCyXhJi0Q88gK6AMZV2v17b0r5Tfm2vfiWIEN3JUlct5sbllLIq2IXV7ElKLViOpA1TrUVK7VpFkrHVdxAwdblyDI7UhDkq7jHjv2TNDfvkrX+ALr7zJeFnx4L/8AfLuT5iEM4y2lOO415loMjTrGl/VdNbSoZhxjUxGxCpHEL1HZ1fYqyVVBKuC+ICoz1HZHiZASICsLjr8cs7F+QnDX/oGr3/1bQ53Dtl5+oAffvw+T9uGaKVv+22dYGrBVBW1MxhXoSYBShjBDgVqRW2gHjiqoUUq8CiLixWri47L0yYZu06wlaE219vtJEZlPJgSxsrF7IrlYsVbL7/K7mDM3MO08hg12KrCGQcxMhkN8KsVJydPsShHB/d46dYrPOuW7AzHLFo43Nvn9vbtFPTY3ePVV1/D/+Pfpfvt/4qwPE/GmxSAPTE1Yz2h+fq3WP31f5Xw0p1krPRnYT8Z2Ygou7BodshJgsjONuYv/yrxa+8w+8M/pv6n/4zhs0cJDIdcPBvGlWU4u6D+/d/lt37t14m33+b7Zx9z9vyEs8UMW9ccHt7C1oZwteTZ8SMGVUqBtDKEtqGzgenOkG7eMJstUz3fyjAYOxq/wlQG30bahadrAq1XFqsGlesD35ZNyx/80Q/pVg1Xl+e0VxeIdNT1IOuKpN+r4SC1tFeoxyPGoxHD4QDrkk57cnbMcnXOK69/icvzORcnH3D3iy9xGHZp/v3fxv7RbxPaJzlAlL5bs+mrkhlSR0fEvUOag13aL3+B+MabhPFOWvM5mMDmvImgFTAcwfYe+tLL+K98GfM3/xrt6Qnm/Y+x7/6E+sMPGJyfYXLBTZU1M9mKwWpkp4Ktkye0//C/4M5f/Vt88/6X+M77P2DGKR+//xMum1PGoxFHd+7ydLjN1bxlawxd1zK7umQ7tlSmQ0wCuY01uKojBtjamqLi2H3pHi5Yhlt1Kjp+rbKmyhf6cPkGWwpKa0g6thzvmcIfcvqQGOkBFDG5u2cMOGMIXlPB0tCmD2+kdgTN56xXqmcL3Pd/invv+4TZE0K3JGib57pYqf0GzKZkWmORmNjkquBqVCo6VWaaisebZpUZHQl0C/GKuJojZ09xH7+P+eVfIrzzMnFSk9IqslMXNde+SIwS7zNIkR2IVEg5MZ8xqehNSa1J5nAGQIptgPZ1Oq79bCSFU4qNYrBYCviWbASR5MRHSWklBcgo42lLIMSkq0RVVFM9mxByCkJGflKTDahnK+x3fkT83h8Rrk7wviWSa/1k/0agTyUvBZN7Zm5eQwlg8blTY0WHYWkNdnuE+oDMF6j3BEmgS2rN3WGe/BR7dkz16Mvor77D6u6U3usTUEp9pcQkCiHvs8zuCCHkt2cLJsRMsjLJEbXlfguwmeqTWJHUJe1aJTNesv/Qqy1N4JeGEiDLDU1y9sP6fWBymQMxJtWly9kLlTgqV+GcyylFkZj9mgow775L9Tv/jPjoIU3bJhuT5NdJvkaPXBshVg6dDIl724SDfXRvjzCdwmSEVI4QU11G03nMbIFeLqhmDTQt9fMTqpCbfCB9OQUpDS1U0eUV8sH3cefnjH711/BfvY8OgFzTBujbdqszfQaGSmIDlT+R1E5eM2jS17SRXL7iz6EuUd+hLoAr9kGuK6V5oiTfg+nrQK51iRilUmUVhRggIHS5y+loVGNFmO5scXD7kIOqQr79LtV3foh59pDQzGiixxOwkhtwF7fVGNQN8YMJfnuXeO82cv8l5N5tqp3tFISytq8BpTHV4TFiUB8JsxXt6QXhwTH60ROIQ9zqHGlnGOmybxR7QCEaRZuAPZnBH/wAljMmv/kbtNMjoO6tqKThFRXTM8D7c1sSExCTWmLEUoKgrE8pKZ8//1z83IWICwtj3QKa/ucXlktRiJqXcXbEDGS0XxltjTh85S00OLi6xJw/x7QzBA85Qo+S6ptowB++RHN4D1+NKEyDZHMkh362aGlyVwCTJ8tlBW9MOrisFNpZRkQVjGhxX/toZfJc83eIEGyN7t1GBxP00/eoT5/mVK902DnvqS5nyTHc3mN7MObO7i6Pjp+zDL1aIvv0FB6NNWBVseXw6EeyT966ftjGGILNwE1G+UrCTCkKXUC4VNNGsv3z/6Htv54subIzX/C3hbsfGVpkpE4ACY0CUBpVxSqSTdXs7mtt16znYeZlzOZtHubfGbvzPjZmI+6MXd6+c8lukt0srVAFVAEoqAQyE6kz9JHuW8zD2tvPSRSrx4oMOgyZkREnznHfaq31rW99S6RCVfp5LvsSpy/y3AvX2H32FXCasP9QdInqKcrVUuoEUtKU9Gp848SorgxwO1u4lQ18URF00XaHkTNPxiMkICuTIDPzQfaUxRUGbImv+rCxizpfYw8fYB7cQY9G6MZBp9uWt8XGCbunqamPjnFNQ3HxPFuDATEGjiYTmpaZknGtBeMoqgysLTovk2cu06QVSyv1rK/0OcubvnVZ5TO1MS1TNuaHUMj6TsYrtplGUbTJO1sr0FWH4fZ5ur01moMD3N3bqPGRvGGTDhwdUd6jfRSqd68LO+fw65uEqkswNoclTxhkuf/cfDN9T8VF+ZtOd2Qq/OY51Po2am+COXyI3X8I+weyX4tEI/YeQiS6QBhNaYKj6hTs9PtYFEeTMc1SCWVI95P90sxxl3sR0GY5F3PWJW5Z6d/NI8pFlAvYUqMqjVNBGBtGuo808xrVxAQop32D1NRardBeMRvV4ggYuWMbFDv9Pn/0xte4NLzI7J0H3P/JexT3blNyQiS3Pk2njgJKQ6M0s8Kg9naxVy/Ru3IRu71N7PbwalFWSozCippMaR49pv7sHv7eA9T+IeZkhJkHrPeJvpz2A1JeU05OCb94i+m9R6xfv8bq7nl650u+/8E73J9NiTbS6RfYQuNioMHhfcl0PKeZzfA0VCuawUbF3NeIsL+icRBioMbTaIjaEFTAWE3ZsVhrqMLZatp0qx4vXr3OD372E3RpwMOdO3dxO9t85QtfZnxyxP1HD6lHp+zvH2Gspt/tUg2GxBBoJg0H8SFbgw16ZZ/53DEY9lhbHRKUZnt3j2svvsTq7ftM/+r/i5s8JoNsclwrUJqmt8Hsj/6Y+be/Ruz3WxuZUZplgHLhEuTTaeFytIAqoFaGqO98k+b60zT/8X+l9+6vMM1MgiqtKGyBagyVtmz85Cf80Rdf58iv8vH+xzQB3NzxsHlEURr6/T6P7j5iZXOHQa/CVrB1YYt5PcHFOWXfsmYGjMYzvBJ2n1eOyekYa3tEb2g8rG5vsL5SpUz/2VyhnhEObqJVZKg9oe/w3iXNwsR8AbwbiZi+80xPPadRE6yiVo2Ajd4Tas/bj0cUuuCNL73GHleY/J//Z+y73yX4A0hZ5RY0T1TsUFVMd/eYfel1/KtfkPPTpPKhVPqxYIX89lwtXxGFNwVqew+/vYv78uvM7z9i9t77lL/8FeWdW5h6jLjQ6ezXEVtVoD2FqRj+9X+k861v0n/tK9w+OYDG89EtT7dXYl3kw3d+jCkN3g0YT2YUWnF+9yrj/Qco9UDWnBZnv+x28M5yfDjm8PEJw16BKiOW4szmUK7kw5AdYLGROoq4Y1QZnEnddUDOJ60SM1DYKEqL+LRL3bmCE+BGt0KTkk0WBzsH7Qozh8779+CtX+IevE9Tn4jvk3wplXzgPI2LwuVk7yT6geTjxDgnophjcWWBGfYIc4ceRaL3BBWkbCh4qB363qfYvz/A3nkO9+VXmJ9bT1rvKUmX/AVhmojHEoJ8Xggx2dxkp1NHLBFBFTArIIw0AU0MKkCh9dJ5cnbz2DY4SXGalCDFtsRAK1JaO6Z5kXVvCznnY+oaGUNAeUepA9qEZPdy9y9JCGIM9vYj+MnP8TfepZ6cCJCSgSmJnkXAGdUyS6ksoVuhVrr4QRc6ZSohiagAuhbhbz9uqILBGEt5OJEkjU5s7BxHIMBamO0TP/gF5ugR1Zdewz13gVip5HPr9izPa05r0eGSVu+0HYVItlep3NkmNxGIyVfSkMRrTWKRnfnV1oqQ3L0F0O6CNP8oEyNYgENa4xRT+3SjCgICuipScgukSUFaD5HMKPLot34FP/wBzf5jnPPUaOroKZTC5rguRDCWMOihLpzHX7lMuHSBuL5NqPpSuq9ycluJ3iaJPRID2tfY+Zh4vA+ffUr45Bbxs4fokznBZdA4LmK+6PBNID6+hfp+TTH7CuHLzxJXRG5Dq1TxoRfJQpX3JwqtbdIeMu3+DSG0nc4C0pThrK+FzyAgpIuOEKDxse0CJmBaSG3cVdK7SqywoiSa1I3NyeR6H5jMa+oQiEYAmwsXtrnY71O89QnqrfeI+w9weHxZ4pwiOtoYMmhDLHuo9U3i+T146gpcuYBaX8UXJV5L2XJ7jsa8DlN8Ip0/YNCj2l1DPXORMJri7x7QfPwZ+tM7FAePsbNTNHMgSXkEGQcfIvp4jn3rBqFuqL7xdfzOFaLppHNBQwCjJS5WSiUiSyrJVAprRYvVGINPplwrlcoA4b9VmfH7M20E8Viq3VpyBdOXcrDF7NeLc9kCO+J4mEKxvncBa/rE8RRODzCzU3SdGRi5l0gUqvvGNvPtCzS2235iIC/vyGxW86MfvcXtR48whUUXJaYoqKqC0pYUZUVRGKqywBSWwlqqsqS0lqI02NJSFQVVYSg0FIkiqtOEawVRaVxvQLz0NBhD9ei+tJ0mRcQ6oudT7MTiTcHaoM/pZMLs5CQZS3l20fNNQXXKnoXklAkolwJctTjzzvLSxgq6rNJzpc+UkUwodAtcJZciIi1OlwPsZAiiCpSDFXYvXEcFQ3h4n3j3FtrPJaioa1kbpkhU1UhQCj/oEnf38FvncCY7bzm4E3HaVLUpH5e6j+lkxD1Ryl2UOEQZFBSiiCFWXfy5K5jNPcpHd4if3ZQ276mmNCaxWwUJsY800zmV1uwMVzC24PHxUWrxl8OabCT0E2BJW/4XdXq+tCTaV5y9W7MM1bRO/NJ6UUjJSERqweUsWEZwln41g2AJwERplLH0N3epeiuE41P8vbuEw4cYVydQFukc4aJ0GbMFYXsLv76J6w5SXWkO2CVflg/PPJb5PAE5I2LaG20rcpYeS2noDfCdiri+jV65T7xzC1vXMp8qC4+n88k1NOMpnaJko9dDKdgfjZN6e6Z1RtpyqUyNTveahYpbEqF6Ynj/+ZdC6KyTOdpJFiE4hfKp1MVJ5iwkHrdKQwC5PWequ3UGN3M4g9RxK01lLeudim9/+atcLC5w+Hfv0Xx4A3N4H8OYxZNECTarEr/Sx5/fxj5zjZXLlwndIc6U4vSgiT6vsiSQRzrnu13U5XWqy89Il7/pBH+4j7txm+bmXfTBMXo6TeVti/OyQ0P/8CHVb+bY43O8/swVOi++zH9++20ezCdM4xzbsZRVgSaifc3kZCqlCUTmtWd0e46PgaoqGHQVRVHQRM/JZEpTN7iJk3PBCCPGuyjlXWd4dTodhp0uUcHa9joba2uMDo6Jekbsw265jcIyPT1kfHzKcGNI2S3odDu8fO1lirnDVB26RYnt9WicwxYF+6dHPPPSq2ztXWKjsLj/z1+jHt3BgLAPgVBUBAx+bYf6z/6U5ksvE2zJojgTssecS2Hb5ado2+oujHv6jfZcUBIYXTpP/N/+B07/ep3+j39IMZHOf/b4CNWtcAE4OmH1xz/l33/jW0znNb94cJNYamb1CbPTwGx0Qqdco5nUnPopnX6PleEKbqY42J9T9CphInmF6M+XFKWh6tZMRyPmc4+Kkdn4lJNiSmjO0EmNATcd4b0juEDjGibTOXXSVMs9lW0uAwweWwDDitN6TvRzyqDRTmG8Qncir33xS1ww56n/L3+Fev+7eH8sThy0WXuxbZp6ZYXZt77O/Gtv4Dd2CXq5Fj8DNPwjB1Cy1583Ly0aLXMYihJ16SLN+T3cF19j+stfUX3vB3Qf3kWHWnwyH7Anx6huiXegZzXl977PdlCEr3yd43PXmM1n7F7YoSq7bK4+5PbDW9y794Dt1XWGa7tp3lSbmLNVwCuHm1nqmaFXdZmezBhUfWJt2oTIWV2q/YN2SecWuiD2odVyCUlTLzFrdEJNRCPMYqw4/s7HxFhQLXOg9mI1dGKpaK3RRzX2Zx+h3/0Zs5PbOD+T0rPWV1pKEOiIKkD1O8T1HnF1gB8OiL0OFAUh1aXH2glL+KSh1yjMBKrRXM4AwBHE90j35UPATA4p3v0lxcE+6o2v4J67QixTuZjAHZLYTKUzMRlYnXRi5H2cjJmy8v2U7FEIYBWVkYy1+pcRIoZIizORunURiEG32iOJs992GxJXIqbnk3klqrZ7llWGrtYi2J60MTzCgAw37qD+7h8Id2/QNBOmQXyIEhZsmiC2NZaasL1CvHSOePkibO0SB0Ni2QMrHcpiKrWKMaKco5pO0KNjuHMfdeMm4d4MNQ1oyeqJVx2zLkmkrk+wdz/EjkeUs68SvvgUvldSFCaxHKAwiU2UACpZAlbAi8QukDKUiC0UxhiU0hgdE+tYgEytFdraf5FYI89RDgbbFKNC2H4hCqhkVPo6S1XI7ytlUcpA6jBLCHjXEKJJ5dgCrgcNNgZ4603M93+AOxwxMx3oW2Izx8wnqcOXBAl+ZQDXr8NLLxIuXoH+CtjUfj2VApKgPVBtOQvKEJQFU0LZQw+2UHtPwUtH8OAW8b1fEX9zG3XsUH6J9aeQ9/M16vAe6ic/xqAIX32WuNYj09V0cvJi0m15YjxSZYfE9dKcJX7unNfmv8XR+KfM3+JP7z1GSTI3JEaP5B31AozTeul+k8+slMRFiSPQ1J7ZrJEw3yquPHORS2sbFO/exvz6feLxQxxO1oQyKIKISQcNtoLNTcLVS6jrTxEvnMcPV4hlKQm69n5T+jqbzbwXcwTb2gZFrAyqHGDW+piru/j7T1N/eBf30afYB59R1CM0c1pl0uSP61GNfuczfPgR5psBd+4pKHoiKo3YC5X0taTUKt1XqihApUSCkffUtNjlE97b56/fH1pNRnYBoMZlvIZ0rC8EgTOQg2p/Fx0Zbm7QW9khNlG6zoyP0bU49jjRKYnBE4sSt3ee2eYeznbJbI9lh1QpTV03PLh1gwef3cQD87pp2+JlxDTTP6PS2EStM9Zgiyqh0wZrS2wl/1edHi8/e5kXr56nLIq0FDSxO2R++RmImurBZ9hUe5iFtNR8hi6m2H7J9uYWx5MxU+dpBcWy8GuuE0a1tZoqB7FttmhRtnVWlzUaI0pSIuyaMjwx4zS5dTZLUaqSLEeuGMpBQVSasjNgbfcqOhjCo0fw8A5MTiC10IsCKYKLxNAQygK/ISLSTdVLayMsBfALQS2f6obJa74disWmjEsHmifrLmQ2jMLbivm5a5i1LeKDO6JCPp8KeKW0BLpWCz318IAYVzGDHmvdDrUfcDgaiwBe+tSQfZj8fwrsFzpEtPMX4wLoOfsrgR1PfJU+NINGySi3JXqQhFPj54ICgLCokjGK/uYu/eE2/vSU8PAOnEirvRgjxlrZC67Ba0Uc9IkXL9MMVnFJSC6De3HpEM2naFsqSAr8WwwhjWHaAosiLtJ7RtCWpjdAX3maYm0d/+kN9OEBKoOMWgvTbj5DPX6EjxG7uspGr0fTNJxMp6JZozy55WsLLqcbSUohZGE61S7AM5y9AH6WSstiDpQ1NIuG69J8XUBAk6xgzjbKw6p2jCBCgEJptlb6fPW5lzlf7HLwD7/CfXQDfXqACdP0ZHK5jiVursLeDvryJfSFi4ThOvOiImrpuLG8wtIUkddaawCR7KeyBgYldPqUG7vEpw/xd+4S7tyDh4/RxyeY2QxNwBAwyqPmY3hwH4Ln2aevMHvpBd668ynHYY4rNVQWZRTKKWIdmM5nBBxGa6yC6KUThncOQiQ4jxrXxFq6Zhkr5bh4qZX3wXGWl3eOm/c+4/Llazz77NM4P+WWucmjw33e/uUvQGtefO5l5tNj1tdX6a4UrGys8PT5Zzi3tsWDT29x7tJVVrTh03s3UT3DYKXiZDLn5v1HvPTK6/jv/gTz/juojiau7uGO9gl1zbw7ZL6yhf+3f0F47mmiSaV25DnKIGzm4C3gnH90NbdgqVrUaed3Wxli/s1fMB0OUf/5P2FnYxSRxpa4gyNM3WDciNUf/4R/97Uv46uSW7NHjJ0EijGAm3lGR6fcv3nKcH3IUXlIXXuqXp8wrxl56eqhtGYyGmM1nBxNiBhsp8Qqh1Ke8chzltJErm44+eweLjT4xhG8ZzydczyeEKMnRJ1o+uK4xxjpblcMBrtMjkY0xxPiyGFtSa+s+MI3X+SZ7ZeY/Q9/TfHB9wle7GFMjrgMq5JW7E9dYvaXf0bz1AtE220dw7YlcTs3S9OU/vHbzl0+Z5+czxbzMZq4uQF/9AfMvvAizX/5Hv2f/gQ7Okwlxg116KIOj4Q92cyovvsDto3lmZdf53Q0YXttgxp4/OFNZhNPz3Z5+coLGFLb4CCdVIzR+EYRlCfEBqMMVdXnwu4eh/snzMeOfq97dpOYrlyAZNtElJSfaET7xLdiKFFE8dO52jrkGjlvSO2HScKU1oAt0EWBUpk6H0Vfbb9Bff9t1Hs/Zj55xNTXBAVVZoBkl84YYt8Qzw+J1/ZwF67hNvagswqmIupCfNQkrqs1qOip5jPi5ITw4B7xo4+InzxAHc9F8B2xjVnAPnqPm03Qn32M/U/H6Nk3ab7wLFSlCGIG0ZWICaTJDGbJZeVICwLSCVLbErRk1Y0y7VoSNrvBnjEInh7oCXsryQsBIpzzRAfaquRDi18uUaGILFtlcMoRkiZIADAWZVNXURQojS40vPcJ5u//C7OHt2l0lLLgxmNjbgWtRHevU+Kv7MIXXkA/9Tyxu0a0BVlf44kHyPo4xqJQ2DWFjYF4tYYvnhAe3sS/8w7h/dvok4ZcMadAMsXRSweb49sUP26gqdFfewFWS7nnENEGTOYMJWkhm0SGI2BNkOYKIXcP1Yt/G0MMAhgbW6AKiy7Plmmz7AdLnEbySzPDSNgFMQFkOVGN0ollolDWLjRBrBGgzTeSqIiAikQtTCveehP7ve/jxlNmZUkMCl07TJDSI60juijhylXiN76Ju3IVqo6U0LSdl+ITJAfD5wrlWxRA5jkqUKZEDXeIw03UlWexL3+I/+nP8L+5g5kK8JnfW6tA9HPUyX3MT3+EtSXuq9dRvTLFSgnYCgsrrVB4n8SvkUSLd4llmvUQgRhkTf9LhBry/hGtbGLui/6kdIqICdfSotUVkH0YooDLCYmwVhHxeNfQ1A1KwZVr57mytUH13l3sW79BH93Hm0AoNKb2KO8QKM0Sej24doXw8nOEq5eIq6tEWyYh6yf3XyYXqLyp4rLXs4wepO8mAFsNO5juLnFnlXBlh/qdbdz7H9E9fYgJM0E3VBBtGhex4wb7wR18502Kr5eE3StEOokBHVPcrNpPzprb2XlRpNbgUfa8vPC/DYL/3uVROYCKGRbKrBpyCU12+kj0vyfjQkWkrCqGmxdAFVDPUONjjK9TACyHdQwQtKLZXGe+uYfTFQsPZrHB8o0F76ibWpBGrZnX87bds1YafD4XZUO47MIqCfJUjLhEH9RaDJO2mt3eV2gurGOtgqhb5LMpe3DpGXFsDu+nYFILahwVZjZCFSVl1WNzbZV7jx/hyGOj28OmjbNDQClPkCb2LV1SmABniZ0i3XqUCG7pZUZDAiTE1c8nbHL8W1wlzWlydnRRMjh3WTp/HB7A43uoySlxPpUMVlkI7c8LNyb0ezR7l3CDdUFR4+LzshMU8sGTjSv5UG9vB6k3zFWkibKsEkIe42J9opJzpqG3SrzcxwxXsbc+huk4UZUVyjXopkG7RhTcVcQOFOv9Ia7xnE6neBInIy78Ch0ziyQHsXGRvY5LoM2/2FGaRyAZwszkyjOqYkLDcx394lBXnzdAqCRWZ+iubTPY3COMZoSDRzA6gvkU7RqUsUir9UAoS/zWJmFrF9cZEHPNMOIcOOdFQ0At1lNM4Mii3WZswUqDXtDbo0pO5OJps+Md01ngV7eIz3bgzi3so/uYphbNJO8SSBsJR/s4DWZlldVBn7l3zOqaXD+dIYnlUqFWkZ5kp+NiLZ7VFUPEzz0mMRfl7IO2B7lS7brXKtX45+/lYCx3xbAaVQaKgWZne4PrF65waXiB8S9uUX98A3PyGOWnoHzrHMWVPvH8Dlw8D3vniFs7xMEaQZdLT5kzPXkvLYCtxb/zA5HKKzWqNMSyA50Ouj9AbWwQHzwk3r2Pu3ef4vik7QpC4wh+DI3HFAXXr+4R9FXuTk7Yb6achoY6CsW/KEq8chRGYUoJq05PZmJwKwEajDb0fImbTqhdpKUTpi0Qz7LtEHJ2Pz495vz5C1Ta8v4Hn3J/tE93WKG6mtIUPHx8h5u3bnLt6tNMJ6ccPzph/fk1toarPC5KVnp91GzG6ekRtQ44E1nZWuX6C88yHI1p/u4fYHaKOrfF+OAIWzdEY2lWNmj+9Z/Cc0+BTlnehEC2sxQX53UL7sGTc5ymMy7NaWaG5hmOgOr1UN/5A6Y+oP/zf0p6AAW4UxlgD/bggL23fsWfvv4q//dPHnA8mxNiQXcwxOoaHyqaxhMiTMc1p6OavlL0laGZicC0n8+Zn85BaaqBJQaN0dA0Xtq+1v5MBaVj8MynI0KUtroSOwig7xphvEVozwsFjB47avWI3uoq08mY5niEjprnX/8Cr77yDer/6w8wv/kBMRyTS6AwmqAF8HaDHpOvvk79nT/Er+0QWRL9Jo97Ph8XIGlLTomLuWnnsT0rF99fJuJk+402hJ1t4n/3l5xeuUT3b/4T1aP7RCzoEu0nSVRTYScTut/9Hhf6A462z3PQOD68+TEPHnyGLgJb69ucusCg08OoAh0NJooP5Z2CoFFVje0rHFOmuhE2wXRKt985szlMDyiaLSolwuRhpZWz96ggGjU+BooUJMmoaAG0QgQHWlkJUDQCPkVpNWyVsBWcT2W9xqAOZuj/+hbu/Z8ydafQqYiNEt21NJfKgFrrwPVz+Bev4/euE7q7RF1JoNUCpCQzKXMetZGugAXowR5q52nCC1/EHdxFvfMu9t1PUY9PoPELvwSAQHQ18eAB5u+/h3YK96XnUf0iBc9tYR46+ZqhTfBkYlmUxgUke5tsoDY2BSCGgBF/+Yx91DwQMWflQsBojWsC0QvrQie/XcqVVJp7nUCbJNabNInmTY2JgVpbGheZp85Y4eOb6O/9V/zhHVyhQRdooPBTjPeS8C0LwoUd+PJrxGeeg8E6TRJB14vDcnEfsNR6OuvkIfGDrWC4hR6uoy9fJ756g+bNX8EHn6FHc3SbQZL3DM7hjh5ifvZTrC2IX3+BOOiQV63SomUTUtkFqo0HxRfTQJC22JCYVXFR6h8V1CG0HVPP+srrOqqWb5N8KiWaStD6fikigyhz3G4HI0wbgJg6ujknHUrX19bYHPQxH72P/tEPCQfHBFNiOpYYUyeg3Op+bR3/2mvwxa/g1jeI2rT+1RMefA5Y1ZJ/sxTEqvRc+TsthUBZVLVKvPZFzNZF4oUf4372LubhFHySe0hsohgbwul99M9/hBp0qV88TzAplgih1dVSKsdfKX2mFDE1e1Cpi10eZ1RmU53dXlRLtiMijCwggZnJpsVFRUGOxQhJDNqIbqtCNL9ciMznjvlsztPXzvPSC9epPn6Eees3mEd3iG6MKgoKbcFnHKAkbq4SX3gaXnkOv3sO3+mKv9POSIp52uxruynbl/xjo7J4tvQOSqGtgpUu5vp54vYazd42p2++S+/+LSp3KkBp2isueMzpHPXeZ8TeL4lfKYire6BKKUH0SsDRTD5QEhctYkSVBLId2iU5jSR98Luuf4IQsSi/yGcGESjLQE0uc1CLNb48KCqKjkN/fYOqs4qqg2jZNFPJZhGJ+IQ4KfzqKm7nIt52yIFpnpxWkyatquiTsJpWGLMIdvImjG0LHeFcai1ZFB8DBN9KfQAy2ES8l00xH58icinCCjFR6o+bThcuPSX1sqePxUgowMpmK+YznClZX1nlZDTiZDoh5gKeGJ5kiASfFlzupqSJUXPWHWsgO6OGkDqt5OeWmEazaD+6PHeZWSOHP1EM4nD3IkV3DX96iDq8j5qdEOspMTRgSnle7wgG/NYm83OXcEWHlg6ILFSPJCdihKiTs5U+U5MPA1rDEpfuOK8NlQ/eFGTKJokUaeUEIFqL3zpH6PUoPv2I8uiA6Hz+EIIK4GrCyQmhqNDdgtXhkFldC9qdQZi057xarMxAWLCpMnCSFuK/hEuzmJ9FIAYLgwKgUh1wiLo9BjIzKMTYAizLV9kfMtjYhdoTDh4Rj/dR9VzqgDPtOHiC1oRzezRb5wim4EkWXmBez/nN+zd4eHySwIYsTCbAqtHSrlorgzZCcza5xWhivrX/tuKIFaXl3MY6nURlDUoRun3U1adRnQ58dhM9HbegBzGCawijEb7soAd9VgZ96qMGl4BhTUz6VEtOBfm4W+axnG3NcAxybmWqum73YgK2EgipU9Ypi84JnT/Pr4DLxmpsR7GxMeTaxYs8tX0FfXvG+NcfYo8XgI10RTColS5cuQhPXYVze8TVdULZk5K2dA+Lv1T7d7aFT66apdfm5R8TAFx20ZsV9PuwugZra8RuB//xTWI9F4MWZc8xrwk3b7GyNuTZnW2qbpd4uM/p0SEzV2MQR7zqFZiOxVaGUEdOjkVkukktWDUKWxRYrVE4AXKUxqYuH+aMd2PjGsajCR/c+JiD8TGnfowLgdHxSDSWOh1ODg8YDAZ89NFHOGXZ2hjwy1//nPfM21TlCs8q6ZI1GU+5/+gAM1zl8tYlrl+5iv9f/ivFowc0a2uMao87OYXocd013Le+CS9cB13INOX9nGMJH7h/7x4zX9PtdinKUhimWsobtNYYY9oWlHLWZzubnLJ2ltO661bC1DgZEz/4NWpei9MdARVQIVLde8AzKzf42vpV3v3Vp8xKy+5Ta+ztbmM6DZ2+xTUFp/snFM4xORphqz6r5/uEOoCP+Lminkl794i0Qp2eNG09f6Zon8218C1cdohjxGqD1wGXzv4QRB8JFGHmCMcTppOAPxmjIpw/f45v/+t/S/ibDyl/+l8JnLQ+ZNSG0B/go6VZG1L/xR9Sv/QaoeyxtHFY7K7sT6VwJm2+1k/JzMPsR6CecLLzOyx8sPg5nEdBVRG//CVm65uUf/03xKN9dCP+l5TLSiLFno7o/pe/59l//+/57v6Ij95+i9HsiDo2jA4dRwcz+oMhnbLH/vGJsO4UBOXRVtplT6dz6mZOWT3mwuYe1aeG2XR0hnO4PGzpMEodJGNIrBiVSlcQkf6YogkdgzCFQUSytU1BsNjKpglEJyKv+IhzDm8U6qhBfe8t/I03cWpKsNIN0eoAqhFAflASn96lefUV/KXnCL1NlOq0E9ESsNWSh6sWAaLYg9RGFwvFEHafRW1eIb54F/frtwm/+gDzeIQJizGISDJTnTxE/fD72ELTvP4CqirkPckUhoXf4lxohYWlU4yncZ6sadO2UlcKF8Rf13HRoOHs5lBYNVkyICZNkSb6VuY1J6kyKKmDvE7rXBoVMdYQfaDxTgBTH+mWJWXXEh88gr/7B/yD2zRVRYwWVTfS+CAm4de1PvqLL6Fe+wphYxeUXYTukTZRrcidX1MZRkpO5eIg1W7p7AwaKFfg6isU25dxT/+a8KM34bMjlJcJVIl+G2KNOn0AP/4RutvFv3qNWMra9S0DPK9tKV/TSsreQvCEppbxUBobrQCTmVkQJAC12giL6kwvGZHc2QrB3sjt22MSA16wshdJM4ywprRJmn1EtJfSsVx61u2v8NQLT7FVT+AH3yc+eiR6Yz5iQiQqLYxpY4nbO+hvfoPwwguETo4nedKXiYtvqOS3t1hNUCKT0kpHJLBHia+T5zgnreLwHHztXxG2dwjf/RH208ei6aK1aEjFSMTB4WeYH/4Q2/82070eXirPKaxNZAK5U2N0bl7VApbSRhqyZY4t8HWGV8x7Lb1vqNE6gUtp/beVBlmjiQQ8hIhR5olyqhjBu4atrQFf/OLz9B5P0W/+BnP/NsxHEBwmBkm2qoLadvDbu6jXniM8/xRxc5Ngy0XcnyYozyVkokhYGgsBc7LuU7s2l44steTrRKWSXpWB7QFF7ynC+pD597roT3+DDcfSfCU9UHAejsb4X99ArQ1pnu1Qmz4qCHiLIoFpAm5HLXHZwm+OKCPNRUIMLVvud12/Nx9OKWQzkZwAtRiYHBgvqGTkU62lCpXdLivrO9BE3MEB5ekBOvr0xhk5DIRhF3f+Iq7qC3iRHq41DlnpHyBIG8fGORrnKUQjujVcYqJZ1ONFqVvWaLwPFIl/N/ehPV1z1l2pSGGllCKLpnoUBCOtGPt95ucuYeZjaGapBbVFGyjinFA1+E6X9dU1xvM5zsf2WUCYBEYZ6ixWHJeIXS0T4SyviI6RIh2acbHa21IfedliAQsDJm2BKHTRCPTWtugMNmAyhsePUONTSGr1xkpGCueJweHP7dHsXcGb8on9kkXeHtx7zHff/BV1aiOZNMhafzSzMlTrzCRPWGdzmb6rpVQil8Od217nKy88TWkEVRfMSdH014jPvAiffED1+D4Lb1Ylg+FRR0d4bSnLivXhgIdHxzQJjUXRZnpVGkNZ6Xn8aPV2FofL2c7jk1/L/cfkuLcZxCjsFFEjzABXZqo9+V4K0EVBb2MXpSz+4BC//wA1OYW6FmG7CIRAHPbxO3s0m7t4bdtgnuTAKQX1bM7H7/6Gm/fuLWjN6eNEiFMulQQ2VeruoZKD0dZDK4U2ErCvrq/xZ3/2HXY31iXozHNvSzh/hWgs9rMbmOksfYZQuGPTEI+O8drS6ZQMe30OxhNyW+p0+qROd0mkGBZOI8LkOtsr6SvoXMaZjG/6W2fgKUXgkllL38saN1ahrdC8bam5vHeRa1tXWR13uf+DH4sQd5ihtHRX8IXB9yo6z11FvfQyYXOXUHYJyhKjbtdPDgDlQ7IeQw4haL2H/NrFn9mZydkFqWem20dXHfTaGmys0yiNv/kZwUv7VR08OkTU0RHNux+wOfgiTXfIUTHn09OHjE6OQSt6qwW9vqUzKDBWM6vnEAL1PDAbO2IQgKZbVpSdkrKRls2dwlBYi0VjzxgIb5zDB4cteoxmIybjCcYHRicTmmlkVE7p9DpcubjN0cEhMzfFdDe4eftTmhh45vJzvPmrn9KrFPv1CdHAybxh78ozdPaPsb/4FfrKBU5jQ/PhJ1gfCbqkef1L+NdfIRrbnkEyEQt3xdU1/8v/63/ilx99kJhKlrIq6XS7dLq5+1GHbq9Dt9uh0+nQ6XTpdio63Q5Vp0Onqiiris2dHbq2EGe714V//SecFAXr7/wSlcoMFYoQFCo6yo8/5htf/SofvfAaP3zwMbPTU+65U6J2qOhwjUd1FKv9kmaq0FWg7HUJesT4dEZ3ZUh/reDRnQknj2agDZsXNqhPx4xH8zPNKII43wTxafTymR5SkAGonEFXSrKnHvx8CsHT63f49p//Kd1bAfO3/4liMCVsX6P++AY0c7DSFnW8vU74d39B/fTzRL1cWpLPZ5J9kTMotLU1+VWLICem/WnSTxbH+hI6E3OQsQB5MpsIpYhGEa5fY7r2Hzj+z/+FtY9/I9FVDBCUlB8Adv+A7t//La/+u/+ejz6+wq9unOJxaK14sH+AOTwlNEuRTgRlHUE7omsotKHsDOiVBUo7io5hdDo50zkE2kAnDaNYxSi+n1KS2SRGoo8SXCSNgZjmWkVagU+NjJXYvoCLgcIoysqgZwH9ow+I7/4E709RnS5F0xAbabygjYLNEr7yEu7Fr+BWLxJMZzE3C9rwYrby2dtOY5o1JSWyOYhSKFTRI+49Rdw8h79yDf/9H1PcuEvRLPlwGhHUPnyA+v73UcMe82eukBl5rRlWEGNIyVDRpQgpox9xSKtvaeCgjSEzIrTWlNXZZvflscOTzCOSfkuIRJ+EkJN/Jd2khEkVo2jhaCPJKh1A+Uhd15hOSacsePULL7DWBOLffxdz9xbeNdigiDZlvaNKCakd9B++QXzqBWI5TO20AWLGhNp7zCXLbYl22m8xzeGTc5r2Ykqo0d+gePnrhJ09mh/8EN75BDtrEkAgwZ0Pjnj6iPijH6EqS/3UDk5LmUZhDSYBkDFGaYetctm3x6Ry8ZBsuNKSFDPWphI38N2Sqjpb0KZdWnHpmeMiwHaJHSZC4Kl0MWRBZTmEVS7tJ6CCNJoIIaJMwfbFc1zc2kD/579B375NrBuIGqKTOTRWkonXrqG/9XXc5csEm9j9rbOSAIi073SaoJjjiyXQQsrzVAtwhhT/hlyBkDoCdgppc66KAfaZLxIH6/i/+zvUB3dRLqDaEqZIcA08/BTz0yHlH36JeqOf5iRpw6T5D4gLH4mScFdamEImgTVB5t41XlhTZziJbZogksZX4WKK81PiPxJRQfxmrRVaRbQVT9pEhXcQtGyYqtA89dw1tmwHfvwm1Z3b6HokiX6CMImiorEF9eYG9msvE55/Gj8Yip+vBWkIMYjOaoypjJVF6WMGVHNslNh6kvi0izhteQ1kUEdplC5QppB6pkGFvr5HMJaxUvQ++Q2FP4SEW0QQXc9HY9ybH2BWd1FbV0CnkjyVOp9lnyKJZEYEiFeZ+G0kZiREwlJ89Pnr9wZtAmLE80YkSouzhZvIkxZIZf9evq5WVqk6KzBtUKNDVDORgC3XnWhFLC1u5xz1YIsQFwdJTEgaJKXq7KREcLXD1Q2ucXmt5WmkZSKk4C8C1hh8aq+rTWqj5tN2VWJQlVKSwS67KFOlhbCgz+kIXinU6hr1xjnKh7cxiTamgkcTKJo5ptOlPxhQHR3jZpPWwSWBQkYpvEt9BHL2LI32IiA/w0uB1la02Nqs6sLBWSx2JaQgccWlHjbdne106a3vEhoPx49Rx49hljtFpYNMBWJh8buXmJ27QEydMLIIc2Y3oCKT4yPufPAePkacl8wm7Rym+1EaYmi1ivLPVTo0QgpkpTWezHr99DVeu3Ye1Smxxi6AgBhpqi7x6nWU0Zj9RxI0akNUGqUt2nvs6TFudZ1+p8OgO+d4MsWnRffEAR8lGyDLOC4Ojcw+O9sZ/Nw7ZsQkzeXyeaQAa5bWm8rd3VtARKVDTRlNZ3WDsjMgjibE/QfSYcTX4BuhZHpHLLuE3fO4jS1hbUAC8p5Umwo+UjeNjFEI+KZ54rCMS/MYU4lQzA63+u3XAFSFpp6MmHcsVa+PHGFibL3RsLOHih598xMIbgGoKYjNlDgZEco1Br0+k1nNpKmTdg1ikJLzSsoOxexsQCsoedZXklJs17JOAI7VNrEGU0BsNKrURDz4dFaUBqfEeb64s8vze88yOID9H/6S/uF99EAzG4vD4awhbK5gX3iK+OpruO4mURe/9UxegzKm7cAX8iEeoT1wM5iTHRuV9nWMSVshtmBrDBL8BQy600VfuEA1HBLefAt341P0yYnUQqd1qPcfMfvl26w+/yyXej1uFxX3JzU1kYlvaBrLfNKh0y0J0yCdtzw0kzlNLVlhep6dzXUpK7OaTr9Lt9uj1BWT4/GZzl/jHI+ODrDTMasbq0ybOZ1OSTXssLa2yuTwmE6/z51HjxisbkA9ZjQ6Yj7zFL0uSsEHNz9idWONx+M5R4cjOiuBZy5cxP3tDzHTEx6phun9h3TGIxTQ7F7Af/sbxE61cByWA4P0naZu2N9/zGh8ShYgz1RqWNjJNvhP7BvptqgxRmONpRz0+T/8n/6PPLt7rnXk/PoK47/8E6KKrPz8x1hfi4OdOjSq2jH41a/4y69+lY8f3+XudEQz15hSUxnDdDqnWqmopxN6m30GayVlz1H2NePDyPHjY2xVYExJ2VUMt4ZUBZxO59JS+oztorEamoBuO6poXAY8NHgn3YNcKjXwPuBPZi0A/dwLz/P0uRfgf/hf0bPPqK1mejRCNQGTHN56YxP/3/8lzZXniLpYItbEvKVkVpTiF2+/z09+9R5O0kRLjigp0ShBXYhRtJ2WRiQD0KK9koILJSD+lYtb/PkffJ3OkhZJUIr63DYn//Yv4P/tWfvkXXL3EjnbJTqpbt1h/a2f8LVXv8j9/X0ezw5ZHW4wO7gvLa21wSiLCgpdKmKlmNcz5tM5/e4A30SGwy0urF7lI32T1dUzLo8CCQB9YgtpsdBBpWCHiEtBjdLIeaVSaRSxTWbYwqb2yIn17TzBNZQKnr60wc7Oecybn6Le/j5h8hBIDGy0BDRGw4U1wre/THjqi4RiXfyKJDIqKyguGBgJw2kZJNmWq1x+kTPpKgWdGUgXEU377CuErR38976P/vm72NolUCF5b95jHj/C/P33MN0O442haIoZI3ZFSSmNQVoQS4AY0Ep8onZ1pYSCtXZRJlVWi8DuDK68BXJ76hiRYD51bwopMRR19sKSUHD6uQSNot/inZO/64ay12N9b5enr15H/cOP0J98IFoxIRLCHNG30wRr0Ncvo//gDcL5KyhbksWbM8OA/HeaB59ioBAk0xiyLUv7s51jln43BhG91gplO5hzT8OfDJkPv0f4yTvoiWg6xPRsITTEx3ewP/slZu1rNBsdKUFBpVJjYb/r5HeqBODktJQyUiaotcZ70eZQyhK9ZzxvnnQcz+jSalF2lG1PUNLFrQlNC0YYeXHr+2UWuEG152v0HqLGh4Atemxu7RDffQf1/gfEOtme4FEYaXhgDfHCefQffRt34XzqBAtGK1wIPD6dMm08tfM0DTgfcd4TvJMSSu+pG0/TeNFubByNj1IR4D0u1DSuFpkAFwg+cm7vPH/4rRcZFuJnR92Bvevwx4o6/g3lB/fRzicNHYgqEOZjzCfvY8/tMn/9WeaVxlhDSCCxqxvmzlN2u/Srkpi6bhEVKmiiFqF07RIj56znUZFY6AplbRs/5XWWz6uUokMlwFXO1QKvc/mnoBPrawN21teIv7zJ4NZt9PREugqnUyTrTPrNVezXXya89Ayut0KIusUdptOG+3fuMD7aJzSNCFIHL//7iHOB2gd8SO3gfcA7R3CO4AMuSGe1/POYAACtFLvb67z2lS+yc/mpxG6MqI7BXN0C9wozHNx8n6I+kd9VERUU1AF99xB+8xvs17cI/TVU0k2NyZ9a4siTG6RHW8h+VZIgCAl4/l3X7wXaZKcgU8RyULMMj0ByPFowIELqqKO0YnV9R6hm9QwzHaPdJNXRKgiiGhJW12g29/C6hOV3jk/eS+ZnhRhwTYPzDS7XQqZBaEVaUsCqEUFBbSwhNGil8CFntWTx5Z73hdWUVSldjzCgdBuoQ8pCKYW3Frd1Dj06QY8PknMsQY5u5pTzKWXVZ3W4wnw+xaUDNiQjaK3IiWlCChyzQU6fc8ZXVBpdWkIjcqvLc/rkdpdNGJY2JEoCut7GNkaXxNMT4uE+pp6Cm4NvCL4hekcA3N4W892LgniToYXQLl2dAg6XShuKooC6EaMSF7/R0t2W0xtISVKm87crLizWXgyeZjbCUFB0V2lVxJU4KKHqUV98GlvXlEdHEIIAT9YABj2bovs9gtWsDoZM5g3BNYTsPKdPyrBlvj+BFnNNtvpHy5DO7hLnXW5l+cbSPZmCEBKkJEXs5AMyo8xKaUzZpTPYRLmAPzkW0eHUZSEH40Fr1O4uzfo2ThftXs/CwsvArQ+BJu1HlbRpsliinPtPAjJtTiMFSu1iTJnmxesj0oFI9kdEt63VvTGo7T0YjzGP7mO8B28k84HHzCb4ZkjsWHr9HvMTJ2U1UbcGA4QuH9pK3dYcnemsteOQnWGVWMxkx1hYNMpoEV0sFdFkh3oB6Rpl6ZYlX3jxdbqH0Pz6JvrWJ8CEWV2LI1IozIUNiheegedfwXXXSS7R8q2gYuTu3UN+9PbHjGdNOwXKaIySgMzkoD9l7HL7XExmKCxYQ0Wh2F6tuHJ+g42VPpU2BGXQ6xvYb75BWFmn+eBDuH+Pqqll/kMk3H+Aqgo2Luzx0qVL3H20z8P5iGbuaLxjdjLHGE3VKRh2e9SNRzvFXDs80MTAeD5n0O/hccxmU0ajMQT1RLB6JleMUt7qa3xdU5kOPdsD5sxOTijKDq899SLHJ/tMveeZ3lVOpwd8OJsym8757OFdMAXHd++jTaC3MuT5l7/Amg/4X7zF4eFDJoeHlM6hYsRXfdy3vo4/tyNj/9sLqv1rPpsxHk/b/Qmk1qGxdeZhQXUGj/eyKrLpVCh0PaN2DcsbMwJ+fY3Zv/kL4mTGxq9/gfTmbKFx7GjMhRs3+JNrz/N/+/VPOWxOKbqGo5ljuNGhmTq0slgN2kSmh6cczQODnXW0g8N7p5T9Huc3OyitObq/TwgK5/2Z4qeynIV1YoxJa1qLJlc6w0zyU2LOzKW5J8LO5gbf+uM/x/zsFt1ihHvuGaa3buIfP8a6hqAizaDH7M/+kPrys6DsEghKC6LlOVQo3n3rPX78d39NiHPZ8zGDoZKlJ5+HiTWz6CSSz8lMa4/Lb83k1ef5029+CVT1hC2NMdKc2+L43/w55n+cs3b3Q5QUOpGTOdoHer94m+vnL/LSsy/y0/d/SNSe+XxGVZQQDWiDMYFajZhNp+gA9bShJGJ0xXTUMC1naCzRnv2ZGiFzrcmWLkSFzwGrk9K6gCQqoo+i0WAspO4rNokQu6SL47zM8+qg5MKF57APFOqtn8D4AdLjN0rnLF0SSo16Zov4za/QnH+RaAcpsaSSKc2dCZPQfiSRmhZJn+jFH5ISmORlhJjWgUr6gp6tQYk1GlQBGxdQ/+rPaYoO4WdvCdu0nVuI3qHv3sH+4CdUf/INfK8URoNalPIsyrAkmSD3oFuxz4iSTlqpTMmHyGzaYM64ir/1C9K9+BixWhEQgd5I6oCU/A+lpLObAM2WDIDGKACA94Gi7LC3dxH10W3Uu78m1pN2/8p+8lBY9FOXsX/6x9QbojNlUmnMqG4YzUPqJhZxQfxW7z3eidZK9nlq5/A+CsOgkdJOqQYIOCfxCjHwhZcu8eKVbVEaVBq9skv1xnfEH/7pbzATt9BPiZ7oprg7N4i/3kS/8TKxVxGMxaW5a3zAeSiMoTQaH3wLBioMHumgqGJAzRsmx6PEPPPs7u2d8SQm+9IG9wuXToobpKS5q3PJWQ76U0IqtS6X5jGkNRpBazbO7dCfHGHf/ClqPBVwjxTzIWyWsL2N+pM/Jl6+nOJQlwBIGB/t8zd/9dd8fGdfWGVO/NyAJBhzIx2VO5LG5HPSPg6RxX7VSFvu0LyGD88RoxFBfWPRqsCcewq+8x2a6d9Q3TxE+ZiNq6zv+QnNhx+grpyH89t4JdUGPnpqH5jXHlXBHCkbC7HlYOGdtABXQS01ITjTaRQgLcV/Xkkyw4eAjZFMXZLXpTggiTtrndhVKWHQ7VZsbW1g7x9jP/gUMzqU5jSpGxZAVJawuob+8kuEF58lDtcAYUgluiSPjmZ874fvce/mR6g4I7MEM5EjEFs/WW5xwaZpQdN2Hhc20hrF3M+5Pj0FnROQ6dm6Fv30Dr55mel8hr77McqftiuXqAhzmH/6gOLZ+0yrHsEpirIkeBIhwTObzjHRMRgO0bbE+ZD74aCMogmIRtnvuH4v0EYl9DOviiVz31KlxNlTKZAWuljebL2VIWV3CHMPx8fYyTGaWlTPokI5h7eaZuscja2eZAzkAD62GMwTAXsWpyJGgk+9acMSbUob+kZTaGgAHRylNcyTD+oT40WRnDelsEZTlVaQ8AxQtRhQbB3eALhuBza3sdNjdAyoKFR95cDOxpiiS6/bozAlPtTpEBFEUlmDCw1RK6HVLgMA/zJxItYWTEKk1ItgNHepkYA6f08OsIC0VNaA7fapBhvQBMLxIbqeEGIqmUuHZ4wQNjaody/hbLHIKCVgqHUwcwAaPG1JT5aLjwvWjwxFDgYiRovjE0JMVNmIzxswnaoZULFlgS2qlA3Jddy51hZ81UNdvo6v38FOJjISzif8TGEnU5qig7aWQb9Lc5qc6PQJOXjO2TPdincKGyuoxWvP/BLErf28/DFtxi6KYCBOEY0IYCuVQLo0DgBYQzncQJuK+vgITvdRoSZblhgThXVzm2bvMs6U5KxfDmAW8YcE3t41uKahbhrM59uCxsXfmZGhtCakEkWNYpm1kx/NaE1hC0zZEcp7hNz+k5DMmC1R5y+JDs/RPiZGQfm1FiHl8Qlzu4YtC8qywM/qNhDLAFhIJWS5Q1jQ+abP1iRm/COxMmVMtZw92iQhZkD6zgd0YcBYERb0AqdqG7h87RJDuqhHj9DzKZ31PtPDKb6updxqdwueeZp49RliNSSq7IQvjLJKc33y6Ih7v3mf0egEH53skwzGpDp0+YW8I2lBmuWHUiiMgaevX2Jr8AXWB10w0so4An7Qx77wDNFAQ8Tcu0vhRCLexkDYP6Rbdbh88QIvXbtMuPspTeVRqeFMcEIHFkYBlB2L8gpUpOxoisoTtaa0Bf2qh7YS6M4n8zOdw6os6VY9gg5gFBe2zxOamlkwbK2uM9Ow1h9w994d9kfHxPXA4eERzdRTFSWzkxFzIv3hgNl4Qm9li9de+QLxNx8Tb30MJwf0Q2IQKUN96QruCy+1LLel3b90yZ4cnZ4ybeZk87n4aXY+YwtAk8+0LJoXFz8I0bdtYln61KggrK8x/3f/mtOTU1Zvfyw2I53ZOgQ6n93h69u7vLV5np8f3sIo8KrGu4CbzGmaBj2x+FhQFgFtIm5+QFNXmEoxm0/o9EqsjQy3K2wvcnxoIJ6c2RwqpSiLAm91sneI6GIKqJWKOB8prBVWbjqQtJIA6cvf/Dqr9QrmnR8x78LxwZTgAqaWtebLkvm332D+/CugCwkqwgLcUu24ylfOBw4ODoAmOZq5yDvT8317fosDvwS/KgEGsl0QE7E8+WHp6H8ShAqAu3Keo3/3r7H/jwnDwzutnh0RVIgUpxOGv3iTL37zj7h9/0NOwpyurbC2R9OExC5o0NpTBs10NkcbaHyNKTpUZYEpTWLpnX2IoZVKNpe2AknOKvEXg0uBfIyYxCaKIUhnFyQakrNYbEAIgYB0rtnZXGPIDvpnP4ODW8TQiI2PoJRHWY++so7/1leY7b2Kp8CkM/Ph2HHvcIZ3jtp7nFdEF0R424t2TB0i0TXEpsE1ntpHGh8Izgk4EIRNHp2nqx3/4c+/zPbuNo1PgVF/Db79HWqg/OlblPMU0KCTvsmU+PGHqKfOE15+gZkqCUFUDJtGGAOdSlNUhbSfVrksxLRaUhZD8JHpdIwmUOooybYzn0la4C37MTFK8BNTOY1O2euczMlr2lhh4IoGT8Q1jvWNNQYuwi9+gRqdpEAutBIAOgbsxV30H38bv3MB1zjp7IL4p7/8+dv8/J1bBB/xTdLW8k7KxWPAB0cgpDKtJJQeco7MId3jEosEKGzBpc0/IV7ZaruTGqXR/U2Kb3yHuqmJP/sQ6xaPFvDMm1PqGx9in7mIP7+FoxB9vuiZTSZMZzWra2uoXkXt5IzRgA6iG+p9SFojyQ6HiDUFxp496y0rdyQlzBaQMSiU8+CXEtw6JayXzsGosji7+OvOO6r+kLVuD/3Dn2IfHxC8T0DjwpGKG5vEb36LcPWKdBBDUZZVijkCIdTMx/v4yf4T9xuX/s++aUznZXvG5rUWl+2uMEmcd2IflErdaVUSHy4x55/F/8GY+uRvqfanrQaON4Z5VdH4CebefczuBmMisyYSAgSv5byYzWiSRqh3DdZYbAKhXGiENZJavZ/1lddtjh98ROYiUV9UHiwiLU0k/SyPp9WG7e1NNrtD9Js3KR8+BDdP53QeU43r9VCvvoB6+SXiygYoK5U1LZlB0zSek9GMg5NTVJzyW+QGpRbzyAKUWbY3EiOp9ut8drhU7i7z/qQCouqUmKf2cCcvUZ9OqY4+BSUMU19Y5h2DcxBv3qazc4Fx2SNo8RlcDNRBM3OOUruWZdZEqTRSkVZjCve75/D3FiJuF6lKgTeQRZsE8U5iURm4ial5rlYMNrbQXqHGJ5jjfXQzBZPGMQlohbVtmv5Kai0dn/hc2ScJ30zoZ84fBp8zYguKa/5Tac3euTX+5I1XWa1KmqammTXMpzWz2ZSmrpnMa6Z1Q90EGh+ZOwcaVqsSIx+FTwrmUheXsstRyoYiirC+TX3wkM7kSDw9bYleoesppZtSll06VZdZUycQQqVaa8NsPmofVA4IcbjOVGsxXRoojWWU4Gt5nORkxygOX1BtloVl11Jruqu7xKCJ4xPU+ER0fPKAe/F4w2CAu3CFYBaATUybXqdC0Jjqxkl1yjFE5nWNUclrahXj8++m7gYhaxPFJ84IYuZDZJplqq/UVuaCJDqbHFUfYyv+6nsruEtPoW58gE1lBJnJoWdTrDI0a+t0Oz1OpmMJNNo1LxtOx4hXED6vmRHPHv3+fIimWAKGsqJuujQLcExWVXbwl6irZUXZX4G6xj+4i5mdkgXBFVoAleGQcOEKruguBXT5XnKAEGUNxICbN2JQG49soiefIJdMkkGbxDZZwG3tC1s6eFlabNZLymUh7fmQDb3CdfvE3fMiSpzqXgmRGBvU+BRVdqA/pCwrZrN56zTJBlRtVLswSQsm3lleuSylBTpUEmE2Oq1dyarLXkw0zIQSxqRZ0Ks6nN/aIzwc0dx7hH98QJyMcfNGHmBtgLp6Ec5fJAw2iLpsQdkMqrfheYycnoyYjU+YT48JOHLrYdp1oxYBEQvH7Ik3SvvKGI2brRN9TYt6p4DeK01YX0NfuUSc1zSzGeZgHxOkBbqeztCHx/SHKzyzvcujyRFjPaW31sfYkroOnJ6OmM4nqZ2nwSpPMB5bKKxF2n8XBcYaKY90EReaM53Dsiy5sLvN0egEHRQPHt2nIbK9vgGF4anLlxkOhgw6HT6+eZOmrhl0u5SUDKo+x6cH1PMZsZFzf7i3yYXVNdxf/R1qNhV7kIY2FBX+S68TVoftfLUtpGVkZZbSxhifnDKv6yVb+bmzSC0KMpahg9i+W/pOyqxlILiV40hT6ra3mPzpv6L6fx7QOz1oWSExKFRTs3LzBn/6wst88uZDxqqhN+xRlgYdhDptbIEfGUZxjpt5To/mYBz9YYWbOB5NDxj0KmGkKktvWFBUB2c2h0opiqqk1BrnPc41dEooCwsxiK1ImVifbXMSZT1/YYuXvvgG8//xl/D4BoeTYzg4pnJzAfCNpn7lOZo3vkUs++RgsvUfM6tg6WiZzeacnJxKzX4bPXxuflofTKVx0a2fRNLY0K0zvfictvNmjkzSREp2MoKG+tpFjr/9Hcq//o90Z8eL1yEZ3eLTzzj/6j7PXXuJD+/c4JE6xpSWaGZoqwnBEl0XGyM2FDSTGd6KFkPTQNM0C1HRM70SKzKv67xvougZWG2knW4q+w6pnCamVKdWEH1MnYmSKQ3SzWbY0Wyu76Dffoi6/QHRz8XPI7HXoifuFDR/8GXCxS+g6RJjSJR3+OjGPf7h778PzQTnBQQVQC6A960+Q9ZZy/pwYrfS1yq2iZJeqZl/4wrsbi8CyhihN0R/41vMZx7z8zcxIbTT7RTUzQRufEy8egm30hHmT4jM6kAznaKLLj7qJRk8RfTSXdWHQNAWoiaiaWLAG7vwPc5kBkHANwHOPPL8JnWLmSdtvUWSNc1T0kExWnTsvIJoZD6N1mxtb8LP38Hcvd36rG1gpyCuraHe+Br+/CUihsrq1jR75zl+9BmPP317cZ8xtnFqe2KmPRnTmayzP5H2TmwBJilZc3UNAUlUpv2nlEYNtrFvfJvmZIp+77ZIKiiNN5p5p8DFmvjZfapzO9RK45SU/TUoGqUYB4WvwdUBF2TstFZS1lM3xCjd+Hw667T2WHv2mja5sG4hZ5CMh9KtmHzjc6mNQrkI1uOR7m+zGUwdzFwQQNOU7J7bozp4QPj4E+Y+QlGgQsB4mc1QdjGvfYn40ovE0pJL2rKfLnOX994CVFi4LguLmNka8rXEC8sJYZnJhX/onEt6YEsWOS0wVXQwz7yC+9o93N+9iZ5D0+1Sd3sEZVCzGXz8CXrvArOdTfaDsJJtB7AdoqvREbqlxRaK0hR0bCm6P4RUyinSH2c+kdA+Yy4rlXFIHcDQAsCJCpiwnVE0bkodG0L0lJ2Kc7t72IM56rMHKN/gygIVjejBBo8vLOr6NfjCy7iNbbwu2r2URlFux8fFMMfF3LX+9NJ5pPL9tU+x7Mkm4CbbRqUJWHxUbYz+ROykFWpYYV+4RL1/Am+eYtUhTalwZSlla9Oa+NEdzPbHmOtf4GSi8F7jXMBHw6xR1AGcmqILKeXCO2xRCKsMTfwcBrV8/Z4tv1W7FjPtTbVUo4hSeQPIMLUOn4KiU9HprqCnM9TJMbqeSjcaACVZo1CVNBs7eFW0g5sdRkVaJzliaXef/O+cF0phmiShSMlcGA3d4YDNa8+zviICpugsMgcq0+ISgulcg5vNca6hHAxFkEiBzRSwtjwstvSqoDQUBX7rHP7WCOudjIAG5QN2NkP3O1T9Hmp6ioopU6YCNhnF/GZS97ngv5xtoCgOjS4KEV3LsxmXzG4bsOYTaxEE2P6QortCPB0RHt3DNPNUqy1aMCgInRJ/8TJNd7h4fzJbKW+/vDClntB5R4iRxjmiNckKLwJL+SK/j5YsaFO3P8sVaQtBV0BL7bVKrdjbZyF3JpD/pccMhJUN4tYu+vEDVBDhQamTdqjZCNP0UJ0u3apL07j2sBBzTJuxDtlzjjn+XzzLWc7jwhlYHEXt/LE4mISTRnLikYM2ASaifB+x3aEEtaMT1GSEqmeydUMUgM0Y4rk9QrffGiTZ9gs4Q5hj4njEEGmcoP8++HY3L19aKUqtWoo4SIATkiFVIe/hRJGNQTrfGGmZGxA3yEArSpjiFXne1TXUxhb68X2Mc6AEDImuxk5G+E4PW5RobfGhycd3ey8hd3JTGWhoXbwzm0JyR63ETBG8Ui3EAtPPSViNSoxwKfOMmMKwvrbORneV+sM7uAcP8YcHMJ+Aq1GlRu/toC5chPUtou084WDn8cr7PYTIaDTG+5oYHWQZ97h0z1EtL732ffI5oZZ+FqNBOgIu9LnyuvERfFXC1iZqXuOOT3DjMXo6kaFxDsYj7OEhO1sbnN/c4jDOqIYDlLLMVA2N2KRZMwMdRG9NCvwFGPIeg6fUoKNBB4X3Z5sVVkqxNVjh5HTM5HTMPCheePE5hoXl9t27zFAUnYrSFARE52muYNbMCbNIUXXQM8fhw0M2Nld57tln6Y4m+A8/JnRKGqspJiNMhGZzi/DS83LWppbMywsq50/yyXR6fNLqSuXv5vlSGXxL38jAUGtDM8AGAsT7JU8inwEhSFckrfDPP8PJV75C9d2/wzZCN49K2iwXjw94fjblla0r/O1Hv6IpAxvbqwTd0F8pUFWH8xfX+fStO0wmmuFgnZPjU04OJlRFiZ/NebA/IaAwWtNdW1lyjM9mDq2RYE8rQ7eqOGWKnANJg0Sr9ozz3uN8oFsVPPvyy5QPAuEnP6OefkY5n0g3orSx5ltb1H/6J7jeWurckj80J0MWI56/mk7njE5P2nCA+MRs/bZtjMse0zJ7J7bbN7tMIQgItRi9XMYrrBKjDVFrZq+/yPHNWxRv/xQb6lbjJhKx05rem2/y4h//JXcO9tEmogpHDI0IYSrpSuYasIWhLEUoJXiP0YFh1cVYTTTL6/dsrtCeNW3RsNipENFG0wRHCE5+lrrMpGozBCxISQqtUsch6Waytr2GGmvUu++gpgdC6VeREJOOyGpJ+NarNJdfIequlEyoRabWzyZMD24SmvEiCMi+ynJ2LvsRbbCZUYn0KEmk3wdomjmR2IrWSymHQq+swTfeYPrgIYNPb8nrC828UzGvOpjRlPKzB7hnNxhHYZHVPtLESJwGbBB2SoyB0hiMVtL23DvmcS6dhoInJHHYszaL+SufzvEQpduXihE/rYUJbfPYZlAy6XkYjc8MmvTfpcsX6I1m1O/8ithMCUo6J8WUHo1lhX3ja/hnX0QUVBedZnIqMAvitqzcNvNP6wtmRluMCzHzdiem5EcGWmOMNM2iKiC/ibynwWxeQH3rD2j2/yfMw1PmVQfX7aEjFKdT9LsfY9a3mF09z+PaE5Sn1MISHh2fErsdjHLgG0zVoTSVADOFaDVppTC2RwyibaTN7y1x+v9nHtVC0DtK8txYy7Rx6LJkHmre/OBjtNHteRpjTNIKojnimpp6OiIEj9KKje1dLpzbRP3ku8TxDNdbw3uP9jWd+VSSsJevEb70ZeKgDwgAmyclElOZYW6Y8STTUKeupjHk4qfYHrI6JRUX4M7ii8yULlST1k4bzrUBaQB0OcC+/g3iqKb+8AH1LGCnM7qzGToE4skxcW2L6hvf5N604JQSrTWFrtDTQ6rThrX+kJVeh0GnpOhYSiPd6qz04JXy3rOaQ6UwRSljYxSn4wllt2KG5b0bn9EpT4heuh/HxEANUQQwXISDkxGz2QSjIxd2BmysbhB/cRNGp3itmJOaIyglccb2BurlF/Hn9ghFB9q6k3wJaB1y0kMtQJXWx9EWdEXAouKyXmSajJzc0KTGNeJvK2OwRlMM1lBFPxEW5HciS+6G1aiNPvZL13FuirvzPmFyiJ3NqFzqpjebMP/lryivXWfMEF8OoQBlNHagoJnhVU1RacooSRybhcHRbVL1H7v+6bs0qOWRTBsilzTERKlV+OT6VZ0uVhWo2UQAGwOhLFExId4hEIcDXGfYaqjkScpepTgvS85GNmJEmqaRkoH0bYnHkyBS1HLgG5PoY6BD0hyJApJEZUAVxKKDqRSmFynlxsit1J5gACiSo5OdJRG7c8NVXHeIOd1HaY+KBhU0xXxKp9OnU1VYbQjKt8GhMQm0IQ1pjMkwq6WyorO55N4Vylo8GnCAsFcWQFFyHlVE2kJKbadC0V3ZwvhInE7Qo0OZ4SiZEAGpNGFzB7+2LUYt+RuZsqdikDbN2QmJcoBKSZs4w2kInrgUUCiFNkrKtII4E8rS1h2GKA62HKCy1W0SgoLkwAUPSy3VTPJmA+Cshp1zmPEJdjpKdZiGqDw6QjkaMTMF3U6X6XhMkzoFsPQe2RtrY9z0jPHzD3RGVwbWWnrw4oZkxygoypLZLKSgLCxek4EbXTBc24T5HL//CDU6IiujeyJ4h9raoVnZwOm0Z8ilku0mZMGAk3mO3rfG0efyjhxQKMXFi+f4o29+GZ0g8xDk9Y0T+rdzHtc4ghNKeD1v6G+s062SJklYRJq5TCeDZDGCNxazd5n6+IhqNkni5fLYejpBj8boQZ+isKJTlLonRRWk9XtYMLZCCpyePPT+uVcCIJVobZncDSs5m1qbts13jFlyMTlCgtxQdSp2d3ap5objOw+I+/vCfgtO2iYOBuirl4hbu8SqL2Jui49v10qeQ+cCo9FYnPEnwroUDCbHJ6vhq6WfR5WhgKUnTGs/Z/FjXGwOKc1RqH4fvbtLuDamvv8IO59LR0ECcT6H4yO60zHnN7cJfk6jrGQnFPS6Q6K2zA8eilioEmE4DNQh4JqAMp5elPKpUlnQZ5vdr31N1a/odiowmi+98AV2VtcwMVB2hmwMenz3+9/Da8u0qTkdj2FtjdWVAXXdMBt7fB3pDnp019Z55qmn8R9/inr8gDgfY4FoDcFDc/06YXWlnZG8LNXyZGSLFALHx8c44WvL/lRgyy6lTcL6MZWmZtuaM/rJ5gmVXxGjTgK8S4BOFAFECjBEQlnQfP2rjH7zPmv3bpEjkEhEuYbO+7/hO1/6Oj945x2cSQCa89iVDm7a8OizA8phh4tXe7gjx3xWMzkZMxpPRKRSa4xWdPpdqqpa6GOdwaWUpuh0BFQwAVt1sE1AcsWqPTfzRwo7SLO2ucpzr3yR5q/foTr9DO+n2OjS0apwZYH79hvU5y63ZXwxBvI+yYFDmjb5joLx6YTxZLTk+LcWhQzLZEe19UVbgHnx7hmmDyyKDoJ3bdtrxQIYl9+LLXLne13GX/8q/U9vMDy6l38qv6fB3rrL1uiA7fUNuisBr8fMT2pUKLGFRhUeCo2fe7xzFIV01VTWQGkpKkPjzrZUcfmEznYmdwTLz+mCsAtwgElCramTVAyy5puoqEGUFhSs9C2DwSruZ3dR+7dQxqOjlrIaFYmFQb92Hf/yt6BI+zNmv0TWC8EnPyrtrzT4UhYCrTZczq60z5PneDH+BjAx4vw8laabNmEiR4HG7Jwj/tEfMv2P/zNhMhYGQrQMJx4zPiCGXxF3znFQbeKVoSg6RKWYhBnDaOmWiiJGykpjCw3KpvM7SEeiNNBaG2xxltl9lUSG8z9V8uVljmbOczxv8PNGAOPU3S1EYcl5N+fhuGY88/jo6fYHXLlymfDr94knh8x7A+oAlXNU8xnKGtRz19Ff+jK+7ECU93syFMypqQUzI8+PSaCfjokxkhmz6d61zj6PbZk7RhtKW9E3iVml9FKcId6MxqLPX0P/wRu4n71NOJhRjsaUTdI2m42J7w6pLl/iSK9zpCqsMdiuw8wmTIC1jqZbaYpuRc9aKpVZ/KkcKwEUKERo5ixnUSs6nUpsPAKKa21RRtNfXUWpwL0HN1ISTsZOx9SAwRq01nSMoj8cYpWiKCsuXb/OsB4Tb99De4NuRAfVlSW1LTDdAfYrXyburC8AUZXYygkci3rJF1mKNFESE26ur/P8tR06haKwGmsMxli0FlF+k5i7IuJtMNamcnZL2d1k0LEtO0TsgHxW7rrLYAv3wku4T+5QHR9g/RI00dSEu3foTQ4pjx2HpwV1jKjoMCe3KJoTHvY3qIYb2H6fYW9IpyrpDvusdAs6paI5O7OIUpqq6on9VYrxdM5wZZVGKz66e0JZzGntQoonrDaYQuaPytAt+3TKksuXL1POA+7+Y1Qzl7gC8NbSaAu9Cv3y88RrTxG6A9pustknToBnCAISQXii7Dd/ubY24Npzz7O5ex5tpAGNSftQWyNYAAmQUiK8bkxuwKDoVZbN9QEkzUeVYp5l9hSFRp9fpbl+mfrjG6xOGqybSaUKEHykuf+QavSIAwbcHQViauxQao0Kc+xkn/WuZ204pDsYUFUVndJSGHOWoE1ml6h2IUpgmDPUiZraolohiRBpeoNVaas3nxLrqTguSotYhIZgFW5tA2+KpTgwtpMSWGyvjGLnwzNGcE1N7o6RDV020oKgmnQQL9DA1u9txRwTXVct2sJlGMMnQ5vrQHMnJUVsHTQFhLKLX9smjo5Q3gMOpaVe2tYTutUqnaISynoeOyNU6yZGSiUSejkw1k+YiLO45DOtEVHlzLCRQ0a3c6aTk5OBKRU1utejKAeoeU2cjCQjrhIVNASiC4Sywm3v4bVNAbw4RWqJpZAVwEOkDfzbkrYE3CwL1CqlWFtd4ZuvPs2glNIYFWVOJLjO+iPC2pF6S8karm7vYrVpM25K5dA3jYYStkYOXUO3Q7O5jflshAopay83iplNKDodmt6AsqpoJtMlCES1BzO0fi+tkT/LWH9pLsnaBW3KJ/9kAQIWxjJt96lO85l+X0XKwUCql46P4XAf3cyXALdAKEvC1raISbeBUmz3p2JxC+IOxDZjIjXLefAzuCIGYLi6xvmrz4iIWhKzVfnNyE437XPlp3PL76kV7aO0Poc4zBpFLCv01i7hs5sQgjBtAuA8ZjbGdDuUnQ71XFpHKzJbaLGniaqd17O8FAIa6mQYBPVfMG+0UtJmNY13BlhUamdvC82g22N3fQd/a5/4+CHMRqjUOjFYjTq/jrp8gdAbEJQc908Ili4BNiFGmrphPJokdlS+T3HQrVKY/LtLw5H3znLwmWdQa4WNShhbS4CNDGt6L21gOERdvkJz+x7++BBVS8033hPHY/Tjx6w+/TS/fvCYuYXB2oCti1sYU3L/0UMOp0eY2ol2k1dEF/FNxM0DZmAolaXUBQZLjGebUZxO57z7yQ02N3f54rMvcGFzm1lTc/jgIa+9+BK/fvvnzOuasmcoC8XMeVys6Q0GrPc2uXbuGnc+/Ij3799mbWOb7Y113Hd/SjGbgGuwSRGgqQaol14kGPME4BY/tz9aPNw7jg6PWuA0h39feOMN/uirXyP6QJM0p3zjqes5deNxzglI2tTUTY2bNzTes9brL014QGmNLWz7vgBha4PRG2/Q+6sHdPyMtjRUG+zRCVdOj3n1meu8M75LCDWdlYKyW4h+VmzodQvCtMYO+2wpzZ3xjPlsRvARXVlUWdB4KXHMAP9ZXBnoLUrpzqGUEp0Ok1hv6dyMKUOtrYg+Xr52jbW4hnr7r/Clo6bCzEJqfwr1hV3qV1/DK9sCWK3/ohbATfJ6Qcl5cHBwQtPUS4ZE9qoxhmHVFZtmtNyXIpVSiu+gtUpBtUIpSVDELHSo4MLmDtkitmwOaIMbARok+eEu73H0wsv0fvwIG+vFZo+OYubovfs2F1/5OsVHfZpwSKdXoX1JiDMcNfNpJHhF0YXVzQqtFcE3jOsxdgXK+X+DB/5PuiQ4JKYkRUh+plI00WOslGXc2Z/w2J3iIzRekgr5iPLeMzo54uR0Row1VsPehT2KcYX7zfsoG6l1H1SkW0+J0aEub+Jf/yqh3EJlHYTkX4YoDEbIY528zeTzlFqx2i2xVsSutZYuMJaItToFi1o6mRpDaQqsURSlZmWlSmyBdJ7nACOPxdNP03zrW6jv/YTOySl2PhY/iYi/O6W4/zH1lR0+mqwQTYGJETvdp39aMxz0GHRXWTElncJQWkWlI0UMWKKIySraEv8znEKMsVglAJ9CWlY3wdPv97l59z73jiZEJWVLKgTp/JnORIWToioFRVFy/uJFBkXF7IOPsKR9GAJ12RFm3cYa3W9+EzeQklNNarec6Fe+Tf7GpW26KM1eGfZ46enzDDslVSEMM1tK90VtCkxpMcZQWJsCQ4MxBdaWdFd30SaVekcl/kwI7fvroou+8gL1OzepDh8nWyC2M/pIeHSf8viQ+XTGZ6NC4hrfUIzuUbgRvdVVqv4a1coqw26XXrfDYNCh0y3oGegqhVGeQtOCcGd1aW1YWV0X+xMQkEPTdvDNkgdWGwgSjCadWUIQ/aYQA9FFNJb+sGJncxN9+yM4OkG7Rt6334PCEBtPuPgU8fnnRTsvnZktPzCdXRqV4o7QVsK37iSa9b2n+fa//TN6Vie2nJz9IYTFDUaI6ORzLvxVopLEtY/U3lFaK7pIy2hyNBSb51C7K+i7jwAlGopECA61f5/OnftsW8cHH96njqCUg/kBDY44GFEfPUBby6zsoBJYVRQVsdNhfIaafcYY1tdW0VZ8U6lA1Iv6j+Ck61z2J6PGxIBK3bxy3FfZPufWN/CfHKKPjrD1FBMayk5FU1g8BtY30FcvE9bWiEb8CvWEvymDmNnEueV6y/pN8cNqr+TlZy5w9dkXQBVt6Wib1Ejv1X4v/Z3nSGf8IoDzjfja1rasrOy2UlmKS7vMN3ZQh/cl+ZziIlSkM57RvPcevee3ODg8pg661Veysymdw9scxBH9lSG2O2CwsiL7s7Ksrq3+zjn5J3SPaosIWPbAc6ecmAPxlt0gGeOy04O6QU0nqNkI5Wrwvp101evii6601TR2IUIac0/7XJe7+MwWaCFIl5sUcOXgUF4kE2USUphrWIRulxVO5FFyGUamsrWIUIugZx8qdwv63NhEQdfDYAVnK8pmLLWziZVU1TVFN1J2OjA6JXccEt1doaOWRUX8x4RYz+iSGj3VtnTNAlCLOYWs1atQqaZZ5rMcrGFQxOmYuP8Q6pmohAPRe2LwhHOXCFVP5krxRBlFSzFNG1nnzQZLjoYc5nmDgMxLf2XI9Vdep9ftokyqU9Wye3S75tIzRimlSQqSoqwv7m3b9lS1QUx65JiTW4a4tkPYf4w5PU4/lPra6BTlbEZd9eh0e0ym0/a+RdNGoMJlvlIex9+iDp35lT5DLf6d/WujxIgk9EycluR4BGOw3SFx1hBOj2E+TRmfFEgoBcN1fGfQMnhb1I2FiGnWD5CNI4BP45bFS2knutVCMbolr2QjrpfuWyGINZqFCFl+1mXdnrh4D/m3Wh4Gwuo67D9EjU/ARekgoCJ6PkPPZhT9gVCiQ02ISZNoScSTbODbDzu7K2THNz9zy7IRhxWTMv1BeIxKSWa2MIZOr8vG6gYbdsjJx+9jJiOIDkxC+zsFnetP4wbreJ1KgpIRbAP8pXuJITKb1UwmEwILpk1Vac7tbPDi01fpl6UEdsl5UfmsTfet0oRKjb4wOwbr62xsbVBYs7Q7VPseEQjWoNZX4bnrTG/eonfUYLxDhYiuHf7hI4bnztF1jhPXcHrQcO90TD2f0dSnOFULezGtW4WhLBUdU9GruoRoGc08REfzedG6f+YVY2RWN6yvr/Hy9WeZnY6p64bD0xN2mxlN7YhRbJ1vhJk4a2p6E8dodsi7hyPu3b3P2Ds2N7fpBWhu3MR6tzg7FfjNTcLFPRlfVNsxow2XYvuH7J3Gc3x0LKxG8pLWXLx6geeef5qcxzJ5PpfOxJyFFBqyfF8b09rg2P695DABUWvCay9z+ubPKW99uDgqaodSis6HH/D6M8/w1lufUPQKtIq4JmCtpxkHDg4csyawuguFVvTXu7gEonjnaaYz3Kwm9CuCO7t51EpRdUqMMvh0/lWlpTCWWcuAyCwMT+0i3bLkhVdeJb5zG3t0G6+cgLC2IDYN3ircS8/jBxu06Etc/NUCJXlHqMXs7e8fpBLF5fMmcu3qBf73/7v/Df3eMCWhUpbfSFIqRskaCtNUL1HCk++rpbS46PRa1ltmz2WmRzKQEqhYzfxLrzH+1dsMT+/JGZSrBlCUH3zEhde/xorZYjKdYEtDLANaR4w32EbmtyikE2jjZtRqwmg84fjhiJWt/pnNYb6sMa3eSUQaA2ijcc5RdTsEFO/c+BRbPCJbaTlbRThdfjdSFhpT9FgfdtnevYy7eYgePUbHiA2RujA0nQFFpYmvv4zbvUZUuSNfOttUTmzkPRhTkqj1DNhY6fKn336Vtc01jLXCRlAyn6ZIJXvaooxkibUuxL/WCl0NhLkcQvvMbQJHgbIF+tJVXPE2ZnoflUr9QoBYR/jgEzbPvcCv7hxz6izeRarRTbrz+1QrWxTdVar+kKrTo+x26A0HVFVBt2PplIZOAR0dOUvuotGGteFKm2iT0h2N0oZOfyCesR+JRpFSwowrcidDBQJ3EVB0O10uXr5EfeMm9vgYm8q5bKePMxZvGuzzz8PlyyRHPA1d0pghxwU8maDK34+K4foOX/rDP2Nt2G81EltWlJJOdKTX5gNRke9VntI1DZ5IWRYLVgjyOWZllfLZK4SPbhCmYkGd92DBHB9gPviIze0tPvj0gNpp8J5m9oBCTQinK0yLLrqoOCo6KNul6A6hKCg6PXq9AVWvw6DXpds/266KMUbq2Tz5vgEXI2g5r3ITERUTCBAjOnpJFitApz2joKkhKstwe5eVXkH85BNMLS3KY2HRzlE0nmZlDfvSi6jBoN1fZH8jLhJUcpb79kzPDCdFSpTpAkXZaoRlW5c7lOp8ni75h+15LkYClexyhCeSKsIa0dBfR730MvUHn6GP6mRLPUYrzPgR9oP32H31EoPxe8xP5gmkAGcM4/EpQVmULSnLDqYssVUlLd2Vwk3HZzmJ4BpCI2eoVIgIwKiUwKMx+x1apX2TfL8EjuIb9nZW6BtL/dlDqukYHRzKSkxWzBp8v4ALO7C7QywqGSm1iM/ahAeASuWPbfC1iK9UjJQKhpVlpdvBx3QetzHfwoNfEAOWkift59GOuYpLr49L/qtS6NU+5YvPML59k/5MAPwQA4UBO3U0799gY+9p9L0p4/1DgvJgLMY5mvkJtQ3MpyMoOhw+6lBWHQoNnWHvd07J751yDHnRJaduQavOD5+NUSBGRVCKotvBmAI1mWPmM3RdQ2iE1ZJqT0O3Ryw7aC2lODoHeCxthgTc+OBb1fgcdfiU2VoEb8nR8gGMsDN8SGUa+XBPDkqbpYhPbPP8wUtPv1xVHlOXjFRiswR8+Koi9IdwNJFjPyqUazCzCbqc0el0ySLGAtpI3it4TyjSPSxr9pzp1cIUhPQEBqFMhvRUGZKK7WBGVGEoqp4E9UePYDoi+tSOMARRve6WhPWtFsDLwFfuPhHamWExytmJbbtHpbtM7Jtlxk2j5J4zqyODYSEKkY2ocfgEsGna2sX285eBlDwKOVjOI6PxZQe/tYsZn6CCFyVvZdBGYWcTTKeHqbpoY/FNFrSLi/UYQwvI5aPgzKcxv7Nqv8rfZLkMKZJ0XFLglckO8lONsR0KUxLHY8J0JM5FWUmQGSPBWuLaBkEXSKeFRQnik3eyfOTJJ3gfnpjTLFiejae1drFf83mSsxhKtftZLSanNRB5/trMZYyL78bF0wekO1hcWUfPxpjgiT6K1kajMbMxsepiiwLX1GlP5AAmpPuLtB2qzvpKjxZUBkHUQnNLKayWcy7GgFZyTkjm1dCrKtYHq+hRg3v4EFtPUV70S0JhiZurqL2LBNOhLfPIn6PzLsynmpyX0+mU2XxCTPXRSknLwp2Luzz7+hfol4O29FApAV6kpEalzL76LTzNGIuxBWjTZisjkTI5bTIGCl9YzIU96t09qtGptGsngndwMqI/HbM36PHg0V0O90/xKSCzCtCKTtllMOyi0ew/OmI2bfC1Q7uaUChghnMOW56tUF9ZlKwP1uiUPbxzTGcz7t+7h8Pzy/fexheeSxcvcXI4YvfcLkfHB+iqYHt9nY9vfMxoPCVoRXcw4PLFi5iTU+L9e4ADlVhlWhGvXiX2emT3z2Qh9xxht2MuI+zqGSfHx4ulpkBZy8rKCoqFE9tOVHJEhD0qdt5o88RrYg76lpwg4sKxiSoS+j3mr75KfftTSj9LLwCiojjY54XqRa6sbXJz+hiMptMdcHj7lNHpnCbILp5PpsIaUJ5qRajWfq6Yj6WN7uRkuiQe/s+/lFKUVYUPAUPEaoMPhbQ/T5pt0hVG9mehIxsbK+ydv4r72+9imxEqzDHtjor41RX8Cy8SjE2aW4oQvAilPpE9TGOXzspA4ODgSLJ8LAE8wOrGKruXn6Iqu4vvqhwMLDFI82/mn7UsgTZ0aYMRiFIikUtyss1OZ5Pf2+b0qev0f/kQgyRCVJAzsRhNWHl8j6cuP8X99z5jND0meOnAobVFY+iWJa5RTA4C2lS4kZQcV7pHfcZMG2M0q/1B6v4EPgaUNsI80jrpdmQ7NG+zxyhSiU2QgCAgWjUhcvlcl0HRI376a3SYopzHFCVlISKgaqciPv0SoRzS6hFkv0XEkECJfkqesyT7g4oKW1VsXn2BzZ2LKWBfsmNtwKeXl4r8OJ+dEXxqnFHYAqVU6maVjo6tTcKL12nu3kZ5T4NoONrg0R9/xM6zN+jt1+zf3kcDc3eM13OayQhVVJiqxBaFdG20PTwW1elR9HpUvSG9/oDpGc6j1opu1RGhWhfQUUn5V4iEIML4qpm3QVQMsWXZuiCBYgbvt7fPMShL1MefUNRzCJ5oLKZpKHykWV3FPvMUodNFGMB53JdIxSoH+4stu8A2NRqLLboY2219wJyRl3pA1b6nUrFNLLe+rxKGlGjz5c9s/0DZCvP0dWa7v8R+el/WJgjrZn6CuvEBexslg4dv4Uaz5DtF5lrRjE7BVmCrNri3RZHe3qBtRVkNsGWXovO7A8V/yuWD5+D0GKMNF85t46YnPD4eCUNk3lCUHV576jybfWlbb43B2hJbVhgrc+G957Nbn3E4qrl+5RLV/JT44K4wGbUBW6DqBh0UxcYO+vJF8aVI9mkpoG8jt7Tfs18uc7YoMzVap3hiEeuFKF19Fi2142KPo5bKdBZMt8IIcNjKmCWgAwBj0Reu0uxuYI7vA6RS+CASDo8fsBKeYnVQcXQ4EsALiB6a+ZTGSdzaFJaiKDFFIaCjNoSmPrM5FB1JzWRSi55NZZhP53SMYXNzi6IzkJhMK7TRGG1bTR2lNZPxhMnRPnsXL6CnnvjoANPMQWtiUaJqL8mpjkFd3iEMB0mqpB3ZJ+OnhAPkxjXLV464Mlux9Y1SvJjtaHZ15Bt5MSx3VFy8yKRSqmVByXb+oyKWGnN1l/HOOcxnR5g4EesfPQRPcXBC/9FdtulwcO8mOsxIZFbqGHGFZjY+AVNgbUHR7WBNgT6sfuec/J6gTdoMSaU6QnLQWzIfi5NoMchV2cFEJZSp+VQCIEVyDiPRFoTBULLByiyyeOlTVPvZsrmsMUnVP5UyoWmaXBq10NxoY2jg8GTK2+/fpNsbUnY69PsdVvodhr2KEgFMMILKyeaUz4xkAEUvELfW7ZFL2Bs5FIlEa/H9FdzxYywpqFeg8Bg3p+qsJHQ5tGrxWhspSVCB1nKg/0XKMiKRxHNrR3cRkGYtA5XGL4EcthL1/tGIOBmlgM0sMnNaEdc3acquqPY/aZdaocMWjFNLQAdCQRWx3NgGlE9cKdBUyEEic6Rb59KnAy9nskCYLyD6RcIEEYBQlpGsk6BSSVjr3AoW5NY34cEd7HSc2rE5QY11xE7HxKKiKEvRX2nP6yWjkDZ1QiTOdP6WBuV3vHd84iXaSos2efWyVyB1wkVUMJmgJlKOQRBWAADVAN/t4VTeE7kamvbZ0pQCWV1ffpap923mL31svgNtTRt4CcgbCNEke7j0O5+/9wxAtctk2SgvwtE8D8EY1GCV8Pi+aL3k09l59GyO7jZC0VVADK1hbqfvyfD2TC9DuxVberfWqRRCqUTlBVQCRYhilIx0u+l1eswfHMBIdGxyx5FYGopzO8T+MNWqK/J5ptXS08TFigghMpnMqFNHCxDjVxaWfq9PtzukLHtPlHBJRrF1fZZA1qV10QaKi1nMWySPbgum9ruo87vUn91Cz6cUMcjxUjfo0xFbOzsMbcW4mFH1C9ZXV5lMZpycjoVRVsPceeYJa9XWsrqxyqDbQ4fIbDJlNp+d6Rz2ej0u7Vxkb2uH0fEJ9/Yf0LiaYDQheubOs722xeXda+gQmE1OGdUN43lDfzBg7h3zpqbT6TBcXaO5eZdyPBLDn+dNl8SrV5L4Zl7neaTTOOaAIJnf6XjGyWic2G5y2hZlwfrKSnrXNH+LVBLLPFq1fLxkm5oC+oXlWHZ+0+uUwl1/msnKOnb/ngRDyT6qxrF28Jin1nf56KP7NBr89Jij/YmwzhIFPU6k64nSit6mQRsYbK/QqRpO9kdLN3ZWl0RpnbKU8YoRo8FajQ2G2jmUUdhohO1G5MpTVynmlnDrU2KsWaRtxPi5S+dxu+chBdwK2hJtnTvCEX/L+XTOc3h4mAKFhfiv1orhylCEgrPzle8dFsFJG/MnG7QsjLe035f9tLj4tYWTnH4cCs3sxeeYvftLevURJCH7mIjn5e1PuPLyG/ziky6j8QgcxCmc1iN2L25gVIfJUUNoPN6Dm07ZemqVW7dKJvronzNp/8glSTDpuJT2QvA4JaB3jKkbHl60Rlr7JOeiTg/ufKD2UFrF4IUtzHGDenQX7RtCAtaL8RSGPdT1q4S1c6gM2f0j4EqMWXMvlTOK4AkLRgYpIZVhv5Q4azsiArksIS4YbkRQIYqmjEqxBQv5AAVQWsxzzzB+802qxw+JGLQJ6Ogx0xHlrY/YG+5w/+B9gm8koFCa6fERQVu0LSmrLmXVwdgSZTQYUGWFLruYost8MjqzGQwhMBqNIEY6pWU6r4kqsQi8ozKaldJSaGkkYoyWboFGUxiN1QVHk5rTqeP89jo9INy9J/GwMgRrUXUNwWHOX0dd2CMnKNvOQUtftxICia3RNrFIm1onSQNywmLJ/yGEVq9EhMwXgX4+J2T+TSoLSie+SklOlcrU17bg+tP4u48xIVCoiHEB5QLq8SMGs5rVYcnx8UG77oOCZnYireXR2LLAFAXKCJNL2RJbdtFVB20slTlbgf4YpUW3SeywXtWhLGqMsYxPR1TdNZ576SvsDHsCqqZS2qCVsDA0uPmUk6MxTZyxub1JPLpDHE3w0RDLgmA1xdSDrVDnzxOG0rEtDbGsf5UZhLRJoyy/KDIXab6QozIoLT5TitYzo1RsXgbdUqlMLr7RYRG3pu+3+lRpLeW5ljNCofsb2GsXKD59hEETm7lICoSIPz1AH52yvj7kzp19aUacxtUT8VHKOVVQooPjanLX5pi7+Z7RtTLo4YLmcDymvzbkweMjdoclLzxzntXNCyhdtiX7xpi2lCgoxWd3HvLgluf8+T3q+xPUvCaogCpSqWAQ/bew2sPubBCKKjGTksfYGiXV2q5IKiEMoWWJZh8mAhhJVD/ptMvGXBybaonhvhz9yrVwfeRNWimPfFPJ2YkK1GoPe+U8/sEnVEEaLOXKEz2ZU+0/5tzqNT5R4JsZOUINRFytiGMl7F5tKDtSNqmL370Xf29Nm5bely0NCbhZQjTjE/RzJYd97WA2JTazheI8GmIgaE0ou4Rl8d3WA1wovOf3yx8dtW4H3y23nU7Ok9GaIjlJD+89YHLyPdCWoAxlr0t/dZULVy/y3DPX2OmXdE0ul5Ip0ynbnzdszBOWDnGdadOJorMAKRSh2yfakljX6XfEolpfUxKpihJXO3m1ElKZ9yFl5PJiWhzwZ3elmjqj0yKOrSaRysFyvtoVrLFVB+0DYTomNo1sjFjIbERNUBBWN/E6AXjJeOXNpNO/Q8uXT69rB20pQljyIJd1MrRnkaUITqjIaWyDzjXNul0Dy2BKRCUEPm3CtL4UuQOLknKtdAh4W6BX14nTiXQYUyKeHGMQRoNzlKZghsK3hOds+EmdseIC1FxinJzZlTfC59SqYxK8hHTWaYWLkew+yG9JBtnYDqrxqOmEOJsSXU0MTg4WrQndHqEolz7vyRt4gh2VHI2slxOi9F3J67k1YhKC0KA5nTsKI+JbxBw0Zguqk6HLCyJnOhboe5v3UEuhv2rNazKooLpdXFWhahEL0zbNpXNYN0d1OtIyNKQCtwQwhrhwrs4aQM17XMUMeCCOncoMsEzFVSi1VKKppcbfliWVrZg/uifdvlL73KA1sdOh3DuHM5Wsy5x9Uqp1RpfnNCLO8ngypXFNO69Sz2vodCQDoLTw8jJg0+5TWmuwFP3FJaO7+LAnbansuczGw1r07g6uP8BPTimaJJgaAvHklOHeHiudLqfU6NJgyhLjYUUbqrJiPnfMT0e46HF4ulWBLjXKKowydHUXd4YMDYCYgqZ+ZXl48IAfvvlj5iHy8nPPsdbfYtDvU89mnE5nvHL9ee7duUVlFS899Sy/fu/XTEODmkVsUTLs9gi3PxZdqcUhKPbk/DlIJa0plHtyRLN+TKI6TcYjTifjNr4AMLZgZWUlsQHynKj2y/Z8kA3b7rzP8+tU+yLIncEWyRtF3Nxg+sxz9A8fYV3zxDqp7j/g5Zdf4R8+fJfpdMz8dNqKVbrGiVNnNLbIQo8l1orNmjWpI52PT66jf+altaa0lsJWzJsaY+W86vY6uNCgNdTOYzFYFIW1XHvuBeLtA+zxI2L0S8MpDEX//LOEsrsYM5XKBtXSzKXzpbVrKOpZzcHhAc5L954828ZoNtbXU+nh4sr+UmtB42J+WlsMv3O8FnZSfe4btICGv3qB8eY5OneP0C0FUjrwVLfvsv4F6FYrmOkhoXJYa1jvr6JWK6pBhVkzTB+NKYuKcq1DrUGXCmXONsDw3nN4eiL35z290tIf9jg8GqG0oXGeAs8zeyus9YWKbqzGWkNZlFRFgVaao6nn9p19ekVgb3sH7p6ix8dEFQiFCKeqEPA2Yq5dJRTdpRzNk4AmS5Y3koCYuBjbDNpn7YX8I9F6S2NNYjexHOgvPqEFClrGwII1QlQUW1vML17CnByIaLhz6OBRIWLu3mX7i1coq4J6NFns+TCj8cJq10UBWkq3otZEo1FFCUUHbImrzw4I9z5wNBrRLS0XdrY4PDrieDSm7HY5OB7xwvVrvPHqdbrWUOhkk4yhKCspEVOK9z/8iDt3HnD+/B7x6BgmI2FQ2QKlLPiaWJbo83uoTn8BjuUYRi3GNF9q6ecZbMvJBimr0WRhpNg6wEFKgkg/SutApw9Z8IOTLx6zWZUNLb6IQhlL9cwzzH/2C8x4hnIpiUgkzsZUozGbmxt8dvdBCvBj0nSKSWheYV2kCC4tp1SXrQ210nhlmNoz7h7VMqECvmmwpsAWBdN5QyBpT6EJWgCrqCWeEmHtfH+aqBW6quh0C5pPj4i9VdysQRmLCYFoFKHXx164gDOabIuW3MYn4jZJ+IuPucwMll0WUUa1+iiy2SJGqaVjcemcjF5iiRy/ZHColfPI60Y+WGXtGgBtsRcvE7q/QB+NF6XIEUwzozw8ZXd3izvlZzQzn84IhC2mFToqOkbTMZquUZTpHj89WzcVg5TtoSSmds5htWF3rcvG5hrKVChMHiqsNSij8UpzcjyjXltlOFzl8L0bVNbQFBatA4VSYA1OGeL2JnGwIkSAhJ7l5GI7Dcg4K60JIZWHf47WHdO606mpR2va8kt+K1Z6EstIb9PGFpmh8/lOcMtrQHcs5fltXNlDTw6kisinUv3GUZ6csLGr6fW6jGcnkoxLPoBKLe+l8YeI5EfdoP8bAlO/1y6VBR+XHizDwiRHIYEcLUINGI0pStR4hJ6cgpujgyfgW8MTqg6+pfv+dinCglBPck4WWyyj4HXTIJSI5JBHQbZtafCNx7uaenoqwWuE0WHgwc3Aw1s3+M0v3ubi9Wt85fVX2FsZUmYHqH0uuQt5/gzspLBQRWJM7XcjKVMDsdshdDrEeiyj4hVKOQrXoENDVXUY11O5TxWxRuHd4t4Xz3bGOzCNq7UFOucAFihZAlqWgzDZBFXVxTSeOB5JcB9cQoUdCk0YruCqLtGH5FTK52T0vP3oCD4KWwCkNjK3q1yuOc1gTQghtQ2MzD1oHylQSK1wZsukA7jNaaTZak/oDOCoFq1OvkxrKHUykDq/Jxq/uoF99IDo5vKOWmju2nmKpqEuShaIO+2aj3ERVLWPftbT2F6qBcfkcRefnMdCayM0TcBkkCp5B7Yo0fMZYXwCoYEonUUioAoLq+tEbWXeFmHB0lfpc9KhGtoAQrUUxsxgQtHOawyBd3/xaz565yPQiqLTYWVjg/8fbf/1bNmRpXliP3ff6oirb0TckIiAFgkgMyFSVFaKLtE1xdbDsaYwGvkwNsaH+U/IBz7ygcYHWjd7aD02PV3WuqYrK1WlTiQSGgEgEIEQN+Lqo7dwX3xw973PRVWSVlW3Nwxx1Tln+/blvnyJb33r8tVLXLuyw3oRWhqmKUqUd+TiPIaxnw6iyFLgODqQ4V5KIXkPhhvIeIxI09Yd40qSpkJLjjGpzzTiAhpQtYfoKevtDGXX4roDpL4t3QzjV5GLK74jtCbUaUqa5GQYRvtHGOeZmwQFSQLDPubceRYqlANAF7D5KwKIngDcMZvOsLZqHXGlFSZNyIsi7MNgrIZ9E3WFDjKPuuRUkPK3Tp3yclDS7SGlMVvbVGvr2JMjXF1iQvCnORmT15bVoseBqxjXJY+OJygD5zbXGRRDxuMZ4/kCEd+ONskUla2g8mVMeZGRNMXfVnCnLqMVdVXxpz/4LqWtmZclOs0Yjcb0sz7vvvsOg9V1zl+4TFnVnNu6wEJqaBxfePZFdg/v8fHtm6yvDBmYBDk8DmShhLXucOtryNpah1Bsg3B+gjsnsXMIp6Mx88U8GBweMp/3cga9gq6EUlrHo7Vv42ctORldQkba+0nMJjq3ZBcrUIJkKfUTj9G88VO0rUMQ0jsxycERl5xjVefsVSPKsqYY9FCNxQZnIpKbClD0e/QyzfTwmOlkgaB855gzlKEgqMTD2WMniSRV9IsedV1S1hKIfhXONqysrrB9/hLuz26R1zMvCyAmtepejrvxpId6h/mLaiTmcFp759RAhEVZcXJyDNKcNju1Zn19fekU65y7ZU3YfnT7bPGrWvq8+IegzwOqL3YgOm19KdygR3P+PHL/ZoADiF+jojDjKYPpiH7SZzFfoFJFOkxxqeZkPsVpx7BvYKdGFn0qXXJncp+ROyLrnz2/VFPVvpmBOAbpCr3BGuNZhXKW2lqwC5574mUuXbmBMolv6y1gTEaiEzSKh8djDidvspYvGKxsYkf3gcqjsdMMPa99QG1rgGzseCLRkOmOzl5EIUe7ObI92PaM6nadUtoT0saMsrTHQCsnjxIIib14vsUzL3yUBB6/+DafHlGoLCe9cQN5722knIJYT76MQp+csKFhsLqGm47bNZLiW4CnNPRFkYkjcTXKKaQS3EJTK6ERhTrDkgy/di2KBGeFflEwKyvPvdVY8myVlbWr5GniO7Hizy5tjEfhK0WSDUjSjOHKCtX9h8ighxNNalJPlKoMUgwwO5cQbZZ2WdR3tE6+iA+oIbTcejHupgAJDN+KiP4NO011qMWYzIo7y0bbvnXyu7+1IGOhQ/ugyC5cYr6xAsfHASkSkFu2geMxW1e2WUkNrrZkosgRb09oIVGavjEMEk2WaBLjb+qUolFCKQ2VFt48Oym29kacS40nHJ7NFp6TR/B2fNs9TVrb0UgscXE4Z8l6QwpxuN2HJAuHrhWqtmijIM2RtS3c9uYSNxtLe6dVy17CSwCDGGmIpVQKjVEmINWW0OVOAqejCqiSrnORCvC2iNACaOkIVJyLWHLsP9OXoxqS7R0WqwXmeNYGPRwCzmKOx2w/dpmvFilZBWliMMEP0cHXzLQhNQl5lpFmGcoY/qO5fXZCFMHa2pPyizA+OkGJ9cToBr+OtG59HififUClPIm/wOrqCoqU8sEh+WKGss4HjEO5UZMa0p1tXN4j4ok+7y61tkhwNiwhIUxIDAY/2tuigZuIZUoMR4RXdQjxsAfCHSXoXr9kl09e5XW9Oj2e9sfEoDdXcSsr2LFgpGntJJygpgv60rDS71EfG8Q1vnGAeNvXOl9lkihFD8iUJtOGH/0WkfwNiIhPf69aQ6DbHW3ABl93nzpQsylSzYPrKMT2Z6IEl2VYkxA5a9qFHhyxGJWO0E84tcdBFFXdhGh5FzkV8Z1molngCdgkZDF8pr2eTWA25YP9XY4ePeL1r77GM1cukukuA44KirQ1srzAXfvUPmIWD1DRCmdSxBQo8WSAYgzKWnRZQq8izxMYx/PYk181IQK31JQ3+KBnZ562h4MxiElwonAuZt/j1WUAFAplUoxJYTqH+RhdRbSU5yRyxmAHA5wxbRy5WyfSOdqt0oww3u5Q+7xn93kCxuOTMT/8xa9DXbUhzXNWV4ec21xnY9AjN+Jbui+tSA8l9s5KG9RwChc3ZcxKhdu74HA6FVRHMUB6A2RchhbxQG1RNKR1SZJlGK2wEh5PYpQflrmSZGlNn+kVDsRgJ3SJ0rgHoo4LvCOxvXRsQWqShBS/N+18Gsi8w7w78cGa3sAjlOJhxOnVESdagoFhVHgv0IjDtuia6GCEXSSec8M1nmxzPoaTRw+4+8F7vLu5weaF81y/8Rg3rl9lc9DHCC28OO7A+JnBfW1/45VI95MW5eHARc9nvcRDYJUopKphvsAUfRKgFt/OWpT2YWXXhjz4PKLpby+/MP5gzSuNh50HY0eprmwq6jYJck+ynDTJUaXFjk58GVGQkO5lyNYabmUNabulnXYaP3+JCE3TMJlMsLE0Dp89TLKEvMgRnVA5qJuGyjrPHyDOc+8kJiAVNEkc96lH7eTh2t+Fey+9zonDDHvI+gZ29wF2Om65w2Q6Q89m9JKEIs0ZNyXNokRnBrGK0WTByWjKdLqgmTtwQp5l5P2UXp6RpTlGp2e+GY9OTvjo/m0mswm650mun7p6lRWTcXTyiLxXgLXMx4ecaMtsPqY2GtcTNraGrG1fZ//oIVvnL5BrjRsdE1mxAvwKzp2HQcdnEx2G5cBltze9gXJ4eEhlA1dY2KODtVWyNKFaTDk+OOTw6JjJZMK8rMiylI2tTba2t1hbXydLMu/Iq3j2LbOSqQ5opU7bABKdm50LNL0B+WLWOiSIRlU1q5Mp5wfr3J2fUE5LqumcwWpONfelN9oYqtI736O9E/KLG5i0QOsGEsjXhsxnZ+coKhRpmnkHTXuuEp2kpGlGmiagxHeqUQpnErL+kH5vDfnsHkrq9iSLrrjdOYfbPg/truy+i3ZTa0Gp6Ob5F00mc2bTaVvZFD850coHbULwqMETYFdVQ9k0fnxGkWd+vfeyjEiL2+2yYKO0AYElvRbVuVoK3kQnNUmozp3HqgTtmvCeEKyrNPlszMZglXpWkawY+msrpAPB1opMpZzsj5HKYe2IyVh4uPcZvaFidho09LeXo1b0einz0uEqP0ZrLUmWU86nPqiBQFJAtoLSaVCwNtiTnhvCJDlKGYpBgZaUejxFemvoxYRUUnSiEe0wV3do8tV29ceOOO1sLwlRUMQKGee6ObfBYVh23EEFfI7ynHRIm0Bo0aUEhzE6u4Lfi629ERwQCcH3yxdpih5mPkKUYMMiMGXNcDbmC6tryP4uPeU7RGVGoYyQAv3UkKcJWV6QBC4fa6BWioUY3n9w5+xkqGLHHglJO4NzltFo7Fv2IhjlSxe9Me9nriXU1gqDJklz0jzD7R+QLSwJiqSuPU9XUuBWV9HbGzR8zklUkc8mnPxhr3TnpurOUaXaJLWE7rXxfY1oGuWDz0oJKb75RkRhx8/wYg2aNcJpg73T6Q1QeY/syhW49RlGfGNOJ4JVAkcjdp68wrezgr6ryLSnX8B54lMdghFGG5IkJ8kyMAlOJ9RoptYxas4w8BauyD0q4vnnjII8Udjad4kVTx6FMpF/0hNPx5bytfhy0XR1FW1L9PEx2XSKdqHqW2ssCays4QZZO/3LydnWHFSKFhUqfu6cxE61S/Me272LpXHCtGx4uH/Cw4MTFpWg05zBsODcxgrbawXDJNqcgJLQzIJW8+pYfcDSOKLu7a0jW5uou8dI47xPpEI34cmEVBSrSUJPLLqJtrTv7he5i2qxlNWCZrGgFiibs0UvKny3YVDMyworXTc8/5Q6BG78+aO1L08yWuHqko2tFdyiQR+ckI3H5HaOjgEWrZFhn2R7gyrxZPWdH3N6X7ZBbvHcVTZsoOgqxqSEaG+z+K5l4mVia2bjKeVkgsKRJAl5v0fW76FMSuR8bO2rKE46JFxUsu3ZHQeqQQ1z9OYG7kGCcuHeeP2sZhXDquT54YAyyzFOyIwmEecDMKlDO0i0JjWKXKXkeco/+y3y+Jvh4STyu4TQRZg1p+KhQpjcyMStsPMZqikx4mu9iG3tjII065jbo9MEPoocJ0epU0VSQBthc0BV1eF3XSYYEWzjiO1z49yLSNuK1igPj23qhvs3b/KfDw6ZfOt3+OKzT9APJUSt9xtu2i0kFbmJaJE3OjglWmOTZMlYBUTQVUlaVSShJV2cKpMk1LY+5cG4+Ixn6mP4D9So0Paztd4IXmS3dKMRkKQkGChLqKqwcF04pMSTrQ1XkNiNSuK0LS34aMiDj7gTEVW+bEm6CNipS4cJmIxGvPfGL2lXQag7HayssnP5MjtXLvHsE1dYL1I6LFSItIpCtJwKZPiuDqFHQBhrdG5UgH9bk3qC7MkRgsXo0E2lrjDzKbrooROF2CjjLmATr/YQOcPA2+c/vDPhuj9EhedFqFsuBSe0Y1RJglQ1djrBVVXgsgkfoUH6PWyaRtMcbxjFvXDa0G+/trpA45poUHXDXTZDunIpWqNUxDI5OmZyfMLDe/e4fe8xvvDSCzyxc4Fc4npaDgHEe8cBuO5uQfM6rb1Msswvm8aiMP7QsxbKstUtbUioPXSXjOiz9fX9pxpCi16/JyOsWWtNkiQkoba1aUJmKhh4aZqRaEM9niHTCSqUZ1gFaqVHsrNNk0dESTxc/6oBxKkS6qphOln4ziLhz9oYkjTHqoyPPzvg4cNjxpMp86qirGts7aGceZGzsjpkbWOF7a01zm+ts5ImS9Sb7fDbSyByiXdQfoA8hfVVXFFgRXkEowPqBl0uSJIBOtG4RGFSQ94voJezmFU0ONLMMBwUzOdzBv2cleEKShusUzRWwJytp6i14fL5KzzYu8u8mWMrx8MHu5x7+lnu37rDuKwYj+bkeU6eJWRZzhOPP8mdOw8Qgf3j24ymU7a0QtcNMp0vrXCFKIM7t41LTPu79oyN86qAmL1VCpxif2+/zUYR1vPacMBn77zLr378Kz74+BOOx2NfNhD2TWIS1rc3eeGlF/jGN77B9ctXghHbXa3+bEf5+St0XVlfo1xZoXf0CBVI973do8gmU65sn+Otw7sYk1A3lrp0OFHkwx51WWHrhrSfkSSa6WTCYLXg4nCDRePI8pTpwcmZyTASbntuCp/NrGrxnS/TFKOFWns0xmxe0xusYOoEdTzCk7j451NK+T34xOO4tGjn/vT8LVmly/owvPDkeERZLqKF3+r3PE8ZrAw5msx57+O7/ObN93j4YJfpaMR8NqNpaoyBolewsrXB9Sdv8NqXX+TxyxfpBfJRv57iZouO4+mwTrS3VNSf4DOC57aodYZpPM9b7NajnSM9OaHINlAqZXUtYfRwH9crvWM5NRhSNla26V/YwpYVo9l9JDFIffZKVWlDolNqZUP7ZIvSiul8QV2WZLnnwjMhKOIQH9ySrjFGE/iEsl4ftVCwf0Ayn6GdwywWGKWwucbtXMCZ9C/tkQ52D7T2sKR1CQABAABJREFUbDiXFS3i0QVbRaMR53m4SgujueVoVtNYSI2in2lWCk0vM6TBKe14bcJZFzhvOgey259OhHRzk+naOtnJQ5yNQXyF1A3peMQTvSEDU5AS5kEc1ngbVqPQTQLOUFaaRmtqpVlYYVpbbH2GpOAQkkvS2f8anGtomoa6CSh969FvnrRfkwQyVBQ4LBafMDZHx57YHs+BAwpRCWptFekVtEZvmDshBlKFziP0jrQ/q9SpuY9vtU6YLmoeHc24dfeARw8OmVYNKk3pDQfsXNjk2oVVzq9m9BNNor2mjJxWSnl/ouX8DHszAuGdUpidHco0oWmq1u9XOGQ2IVXeGTRWcI0PONgA2zECifIdmupmzmwyY2E9N2TV1Cwax/yMuVA0kIVyJZTye0QphkWObWocDdY16BAA9u3OQZwgyiHBzzRKkxQ9KCvUvApIRb8+ldWoxCDrQyQ3nUqN8llCvnUa1n/XIQqXHHH8vrK2YX98zDsf3uatt9/neP8hVTkPJYue5ykfrLNx4SrPPv8MT18/z7lh6lunn3L049cl1PaSD0KaYc+dY8zHGLEYiZpZUOWCRBJmSYqxU9LQgMIj5/16U0r59vVNybRumAp0MIWzuZT4UiytNL1ewWgy8U1GYpmsdOeZUt4e0uLdCdvUDIaryKwmm1dk1mKc8zIX8XqmnyCDgadH6Y7D4E8t781uTsUBLibol41HHxwUZamqBcdHM+7f2+XB/QccHh5QzWcgjkQbBr2ctfUVzl/Y4uLli6xubqHSHpD+FbZy8FyCr8jSMBUgRQprK1Q6IbNB34t4PtV5TVHXXO8VGJ2RuhJjw/sEJHS40hjPT6YFVf/2tu1//aBNV7jbGY7t4fC5HGpUNuKzHa5pyMTfVCE+eKMTnDGIMq2AYiC6u0f3cTGkoOK9wgRa2xCjbtEBVEr5yJ+w1IqvxQigE40xhl5vwKIag7NM9/f4yZ9/H6MdX37mGTLVArs71yeexuH5/Lh9iZSECKRTYLMMq7SHlGpB0sTrJvGBC600VnlnTBtD01QertrNYMy3nvHlMzDG6ECDIf6w0EvBp4CZUSKeSKyxqPkUV89bw0aUj67qJPXQthiwieMX/3ldsVR8Iu+exgBCO8NLB2c0gLWK3XwCDF9Jm6Wy1mKrBbOTQz55713e+c0OX371Szx7/RLD1BBh5D6w2MnQB6ygLfXQruUwEec7lynlI7Y275EEREHL3NBUmOmUZLBGSsJCKv98SlpDV5QKjkpwTv+LyDF8ur95mPDPOwkqwD1Na8hFZZFojUynNNNYMkRQlOGQ7Q/pKMdpn/H0ToROcBFlFfaADWVW0cgJsO8YX+lmJB6wwSH124nFaMztt99l//4uk6+9xotPP0Ghu3IDP5auJM6jCRQtBJKOQ0kA0gzRCWL9oa9Tg0kTLBbTOIzSYaycmsO2TPJsz0KAruMSOnQy8f8rpdtSqah3lArZxRAkMdqwGE9wTdOubWcUZthHb25S62RpD3Su/ucmH/At7cuyZjabIdK0tcKNdRyfzPnNmx8zPXmPk+MTmsYj7XzWOhTeKYNJM/L+kLWtDS5eucRTz97gsfNbp3Roi4BaRvYF69Pr7aAhhgNcltOgaSnZRJDpFJ2nuKZmPp9hVU3pakaTKUWasLGyys76JfYfHXE0OeLSpUsUWZ+T8ZjpfIIVIYklK2d0FXnO77z2Gj/7ueWje5+xc+ECvaLPx59+wr1Hj3Aq4fqV61BWHE7G5LlmdXWdjdU1Hu7dY/94D21ST6BaVpjGNyXXYaNaBXbNB8WX0Z6KzyMWo3y9IXe8f9hCf/2ca452H/L//uf/koOT45ANisaTV9+Va3j04AH7jx7yzrvv8w/+8d/n1Ze/RGrSNlGztN27a7mWWPBnRC+n2j6H++yWzzATHTJHcnzIpUvXmB2MsZ4khfmsxNaWZjQJRoCintfMrKOap9RlzXA9Y2Orx8ZGyp1fny1xptK+Q02SGJROyLUhy3LqKvFkrlUNaAaFZvPcBnayIJ1Pgk7rdKKkCfVj1zxXw3LCp502wcO5Vbcflv5+dDJmUS1oJzPo7DTLePfmPX75z/+czz69ibUTkKab+2DCTo7g0QP45O03+NGf/ilffPXL/N7vf4snr14lC00coDs2ousSOQD82d0VzikPo8SurVAnGfkinI94PautYI6OyLbWOH9xndXtBFfWDDeGTKZzJmXJ0PQpVJ/x3giXwMaF86z1Eu5+9OBMZQiw3Lbc4VDK+SSdVlilqJoG2zQBHS2eFBqHMUmncwWaqqLXX0dNK5L5CMpp67Q7HDIoUOtbS12G4hfVOmUSHKs2oSP+jOo6CHkdapVjVMHHuxNu3tpj985njI4e4so5RkFW9BlsrHPpyiWeunGRaztr9FMd7Nz2yX3lWnD0nZXTaI0ip9w8D7c+9EG3MATlhGQ+x65vMrFC5qx38JWOJgFCQmUt83LGSVNjlfOZ9EVN4yzqTB3+sDc0Leo5MQmDfs6iqnCu8QgcOnPE2+C+/CbwfmO0wTgH8ymCI5YTAR75vr4WyOLlVCCmdcZUdKCB5TJlaW/a/lzVjvdvP+KXv36fzz76iHJ0BM6X1YtWgOZm0iNfPc/Fx5/giy8+zVOX1+glrQffncvB8VbowGO0hCZeXWGaJrh5RaIgCTLM6oZEp5RZQWJnZEaTpgoVCuKbxp8OzlrqpmLSNLhewupqzuCoQotlftY2qlZkRU61qHx3X+URzybxZ12bWHcWJJQHQiCRDYTP+KQRCOVsSmH9edhVPgAmRfUjIrxDO0aX3q+NroojIn9oPyPYiAJKOepmwfsffcIbP/0Jn376KXW1IHKLtXIooZodMt67zf2P3uH9p17gK69/keeubniEmgo+LlGeQef7d7fnslIGvbrOBMGKJiHI1GhwjkJlqDwNKCWNcYKyFY0T5tKQaUWuHD2cJ0i/co10dnSmYnT4agKtFWsrQw73Dz2aJOTsY5MZf5gE/zHSYhiNVgl2svB+VAignUrWDnu+43Lw9SJ6OPrrEnzu6LcQYgpE0ABLcQIBcZbx8TFHxx9y84N73P3sHgeHByzKOZGkWuGpMIo8ZWN1wOWdLW48cY1rTzzO6uYFtAl0LZ/T3e1yWf4ZIDXYQcEcTc/5ILwS5xFX1pI5R5Kkfk/bsAI6cy0Eb/wabZyjXPx21NvfAGnTRSu7X3VeTnSRlATCRK1InEUtSsqypmwaUoRUINPh87Rpp10Ijlf4oSPGdZ1DKFGhSqe0myYQh/qfjfLZ6xTdNk9Z5nTw/GC+bGQ6m2MDE7VTwnj/gJ/+4EesDYY8ffVqWybZRrXD+HR7CHRZKYkHjlKgE3CCDi1xlVOopkE1Jcp4wrvYicsYTbPwh25LOBqe+y9P+BldSerREIqWmNdvPLUcPEUnKcxmyHyC2BrlLCqiS5TC5jlNltEe261dH2h+ZWnVnIIO+9eI8hmm7jG7wyPCzOKvO4JUwiEqENBbh/fu8P2jA+4+9yyvvfISO2tDjCxFYkX5zjxt/V0s0wvZRxWJbl3Hd1P0/JidDzJKkqHIPDmxcwHB0mVDW/tZOjpuFw+OM75icKR9PKDNtreRkWC4qdPz60v/NFIvIJLYhuyacw6SFJ3l3V489ZRLl2pvH1HaYXDO88NEgwhaoyfKLWYn26RI2Du+ZjjMv7WM9/b5+Q9/gtaKLzz5OHmEr0YHth1jtyZlaTAq6owk8YFGJy0xtojgbImylXfawrwq7d8TDfKW8Oys92LQSVoHxI3SbctCrbweA7CJL20ziSfmS0xK4hQyK9HLaCZtIMsh79OhjzjtFUp37/jFOUdZLkK7b9e+p6kbJqMR5bymqTw3mJOmExp+l4OiqTT1Yspiesz46IDx+Aj92ktc3j5Hbky7Lr0JG8kYOT2vUWZZ7rsKKo1YPKeR0khZoesGmgYay8rqgAaHxdFb6bF1/jwr2Qqqzmiamn4xZNAb+sMSx2Q6DqRvZ3eJgto1JBiuXLocygoSdja3OTo8JCkG9NOMSSk8+dgTPHbjBk/duAHNnOF9zfRkwt7JA+oamkVJEccX115oj+mNHdeiUqMhA0GXLnGT1XXN8WjUnppxrzx6sBuM4eUHCHtSq0Bu6clAH969y//nn/0LnFJ89eUvk4ZgV1sC0nqLilMIt5gg0YZmuBpZtNr/EdDjMeupwYiiqWrv2CifKffEm5CkCcO1AU1dk69mzA9mHEzHDFcK0qagDujas7hi5wuVmtbUN4mhVxTYuqBxFp37VVvpmv5wneZgRN4siGe0D2RAk6dw7kKrX+IcRSewtV+iilKdA+KccHR4RGOrECCI+lIxHs/593/y75iPxwiRzLA1/31ZU5CPV1cNi/ExP/3e97j54Qf8g3/89/jmV75CGgLfvulBh56M73WtQR1cniA4m2fYUEIm0qUhRAGzKcPHUs6d3yBbE6qyorc2pF4cM6osd/b3We875tMZ1ljS5DzF6pCV5Le3Nv0byRF8JyVxNFVsbKE9p0eekSpNtRjTSIML/BI+UuwgCeXV4vVwkhi0LnBliQ5k1NiIllGoooBeP2xFQSJscGkPSGjjtBykbjvZhPFWFm4flnzwwUd88uZvmO7dgXIM0oD2LugMzcmu4eHNnJu/PsczLzzP6698ga31Ab6q1tshLqBXdevdhPWj8Pb42iZHziDiGOBJRrU29OoKXfiypwT/P6KoK0vjGvppjXYWaRoWBnpP77DaX0U++AxdLsiPzs6+0VpRFAVNVbUJukQbksJgkhnOWZwTGhFM68wJoD1XjChS40sb7XyBrp1HK2J99jvY58nqStseWmJf5mCrRvt1yaXvkA3eMwwCNMzmC37yi3d5/623GR89xLkqfpDXyS446tWC2d4Jt47ucvDpxzx6/TVe/dITrKXhTAydTKMb+vlAASLQ8yUWY5mhtWbFOVKlsc7St4IqcrQTjDiMCOCoRJiJY71IyHMoKlBpj+zLL7G5vY57+9eowyMqbWDvzMTo509H+9jbxh6dl2C0CeVJXammwvuLrROnvc7Msoy03yNrTkgWFabxpK2xZByTQK8fOPegS3R1XqkXwxLSJnL20aEi/B8sD+98xKPbH3BytI+VTr9GjtFYeigBKWJne9x9+8fMDvapvvMtvvjMBbJ4RLdJUiGisk7pAUD1BoxRzJwiSwzDpiJXmlIciQgYTb1YkJmG3BiSLCVZH1L3UgZra/SHBc3RPsmjMW7nKuaTD89SiIS6CrTW9Ioc3bbB9s/vW5zoVobR4nAaTJaQFD2q/QlJ47vS+pI0CZ6WQg/7iEmJtovfyZw6k2j/SgjaxHNqOWDj53g0XvDWOx9zcFSx+2CfxWKBE9tKP3h4NAL1omI6L9k/POb+7iP2D/Z5/gsvcO7ydUzaB7r248uBqfZsjPI0iibLGKmUhRgKY0htHXx8SMQiiUbQWCskibR2MA5K61DakIbEkfz/iIH/tYM2vsbZL2DfNUlap6idWQk1utp6R0MMma1JpGbhLJNFiSg4VyTBqQ7OUixRaaN1HTS3zUq51tcI9/GKaWuQ8uzlTYpMkycpSZqQJKGMy4mvTawqxtM5J5MZs7KhCWN2TYX2KQqcCI217N/f44c//DHb/2iLrX7fEwxHxa5i6ZZqxwqyBIsLqy22nsQhrvYGVt2gmgrVk7a1tUc9GFzjFzTa19cpPGLn7PlQ/KFlEuPnQPuARiQRjWdNFKlWCjcbo6oSJ+oUv48CyLLQyeu0qqRd1Gr5tnT127RlEcvdapbLZpbSSCxFRIBQO6kUvV6Pum5YlBXVbMrNX7/JeDzm29/8GlfW1zrF3Q4txOKXnX2llsSrWqdU0gynfZ1iS2DlBLuoUGsNOkkCgaM/ElrEYOc/dRnqs7yWHS91+ueOpmB5vmIGwq9zHQ4hW1U05QKxQiIO5STUyYPo1E8NtJBACbeK5KbtkwVHIa4NGw5JT7Id2uQq3bZtb8fqxPPehK5NCB450i0hRByToyPe/PkbrPYHPHlpJwRcw8oKmWAV1iYE3dQqaRUUifYZAxees3GIc6G1rkWh0eJrZWOXKx/p13QhuDO8FH4+NG1AuSWIVZ//Pzpm3jDRgu8gMZ+jA/O8oLxDb1IwaZjDsKfD93+pSCrI0YmjrEqqqvTGYsguOOeoy4qm8uRqkYp4mbU/rgwRh20s1lY09YLGlqytDxgWORvDVVKTRPXZ/huvuK7C8QGpQYzxpZMxeyb44LwTjHhnTOsc28xRiSbvDyiGq/SKdTa2HUejfYxOyLIeQ0JmzwnzcnamYrSu4f3332MmHqVRNRXS1NRaMV+UGCeczAY8+8LzXBwOWVvf4NZHHzCXBQ8PHtIf9Dh3foOtzVWYzUhHxygXyuHijJnTxoPvqahOOdytelOKuiwZj6ft3KrwotMBm2BihfXlwj6KclUCk6MT/v2/+XdcvnSZ6xcuLslPWkO23YfxZkRDXOFWhkvBnW4/UdYM0pReUXA88wEKbTw3krOKtJezcWGN3BiaRcV8MaUpaxqjmZUN+7tjysUZBm20RpsswLsTz9lhhSzPMLMU65xvtY3CpBkmzT3qi47/KV7WJJ4/63NnYTfzwW5QsQxTwtno9+Hh4THL2XzBl3PXVUVdHxPN2nAKcjqY3gUO2n3pGvbv3+Nf/vP/gTQ1/M4rr5O0ge+lkyk6sFHIbTIu6I8sx6aFt1GIqFKfAKOyFP0eepYxWUxZnJRMxyNm1YR6YUkax+7dh/R7GRcurrAYT3nvkwdcOLd6BtLrLqV9W3tjEura+ocJqKbUJOjcUJaTlsgSXOA886RiAohzGK3J85wsMci49I7iksycUl7Gaa9DKEWHfmnOvHoNejcmoBQhceRfPZpW/OAHv+bR7iOa2T5I40kqIRCWKp8wkgaxJSe7E351tMto/xF/+EffYXNl0K4f3UbCuxUZnUYB7GDIiWis0Vit6DcNGZrGWiRRVFXDwJbkSjAJMEhwmWawOcSkBeWjMdrW5NeeYrBxAZUaMpmS7O2eoRAVSZbSlCXOCSqgS7XxiQtrvdPnQlDDc4borizEQZKlFMMeiRakqkls6PAZdaXSqCLHl46rLsAabKfI5RTRjtHFca1d4G8lCMcHj/j1T39AOZ/4PRH/vvSaVtrKYusJJ48+5pffn+BwfOPLT9I33sZqy6GWHERovyBJ5jubpikmy9BVhXG+vC9xnhi2KRcMtKZvIO0VLC5uoQY5g3Mb5L2c8qM7bEwrzOomve2LmK9kGNNgywbeOTuHXwGG5a6svnTGBtSP2JCwRqHdcndMz2kmznqEXJaS5gWUR+CaUAMg7bmiAJVo74O0roZ0voO0Fr9fHuK5bDqUmmr/7nCMjx6BcwGBFT4vbFwJzlGHzAoVADLn6MEH/MX3YW3tv+LJiyshoLhceBXH2zn8oCDNSNKUYV6QZin5+JDcWYzx7y2GCVbXpDohNxp97hy88AJ9p0kHq6ihwRztsXrN0mxfQP387LqAxcSfwjdcSBPj9REe6dLa39qfSdGPiqRCJjX0Vgc09TFZU2OcBdWFz0RAZ2louBH9ynAtIVGjHL0e7WzCbnLDfCrF4fGM48lnzOa1pxWIIAqJZ95S3YN412BeW+7tHbOoPsQ2DV9MErYu3WibD7TjiUOLb4/3VtAkhjLNaKxHRzGdkyp/f1GATsEVuBrAkip/rpP2sb0eptcn7ff8+tg7+K0y+WtKNzjsyrX8NT7aGZya1kd0EAsrlPacH1WJHp/QF0cRHiK1ClziM5/Km6GdgxmXdIjUCu2GcUBdOUbHJ/R6Kb1ej1dee5lXXnmKtN8jzXoYlaCVpnYNCkvTCNPJBKqKo7099vb3+fTOfXYPR4xD9sQn18OhbB27t+/y0c1PWPvC8+QxwKLiAlnyKmPgIY45KH0CBFDEghhf32YdxgFVc2qBaqPbYEU0wDvlfbZXNOyNMZROcMFx7P4Yr5gBMLjZDF0tIHRlUdajFVAaMalHpYYN0AYN4iZTS+6iRBXpxyDBkbBLQZtWxanY8rgdTuvIyNIkLcrK11iGz2iamrs3b/Jd1/BHf/h7bPczot9OCDYGhoVg+jpk6SYSPleJ86RWKHANYhNQHo7uKt920ZNY+X0g7T0UbfZVpJ2Ts7+iMfg541uWf4rIL72UHRVQmsQk5E2NVCWzumFaNygRVoz2e0EHHpfWj5NWflErRyZ8wZciucYF1JJia2ONLz77OGmRU/R75GnmWweK83KvLLPZjHlVcnw8YjKdczSeMCsr6pZ8x1+usRzsPuLXv36Lrc01NvuD7lFbpzUazEsGGEtZ/jgPzvpaaqQ1rE25QBWmI132ll1rhC/f56yumB2P2fQWidIiYHTbSlTCANo6d/Bok8UiBG0kvFf7oK82naG3tBY6ogW/maIOstZRlhV1XbcGartWpGPpN2FMcVsKYF0gJW4NXIetK6ZHh9y6+REXL2xTpDlJL7BILBm0dFPd3lfA810YQ+zgJYQW8s56oklt0ImmctYTFSYGlKayQmWFrN8jSXMa67Osadaj3xOPLDxjlVotKj65/xmvf+kVDnZ3eX/3LlorenmPxCRoUi5fvEheNwiG6XjCo+Njau04PhlRS8X6+TVWBgWJ4JFEhOdWwcFYbmVKQEdEIYWvEdWowpjGs2kQteoC1tEXiCJQ3Xrz+zKal/F3wu7tz/jBX/wFl/7hPyILXfui5ATv2MSyxHijyB/mBr1Qqx40rPOGtWtq+qlhc2udUdKwmJXegIpIBiMoVzM6HGOtZbDSZ3BthXS9h0qmzCflmR+NSeKh+55c2/hAjk5IkiSodOtbgxOCSycjlLUBsdKd/6K8A7G80FoHfuk8a43O9lI4B0eHx2GP+DXQFTgSNG/M9kedFtbIUkayg5lLe/aMj0f8q3/5P3Fp5wJPXr0RzuOIAPIGVkQMd0/jX+NUsDmTJJx1IXAYXu8UiFMcfHLISXVEkScobdEJNAuhqYXNc31W1gqKvqJp4PLONkl2lhKM8xt4WHQCEvhPjPaErDoGnENmQnmkN6hQkhgRAZDlOdnKCm4sSFOfcsgFINH+/G91N3SJpZAEdDEnS0iKydJa8O7nfL6g+vRdxDa+O8xSRt5BQMWGE04cgqVazPjg7Tfo54o//qM/wOR5ZyvqMI7wo8+Fe1rjJMvY7g8wQ0WaaNg7JMWSKdCpL9fvU9M3YM6vo555CkwfvbEDKqF4+Ck9s0AunSPdvEJ67ZovYP3eB2cqRa1DObf45KZJYuIHH2BSurXVRftyYhMRACIkiecO04mhCSG6ZXNaoRGTdN6G8mXWHerDf21RUdJhFr2zHw9XR12VQOlXkIJEeQcXcTQOGiJ+xutIAZzUzEcPeOsvfsil8+s8/9g5jHgS289zcC4ftkYnDHs9tjcM2WBAebBPNR6jEs9bs/PYFuX9W+QzITMZsrND8ZXXqB+OMevbkCoGL5xHrfRQG9uk+RparqPnDYsPzhah0Q6fYLMogzI+ka2V8hxFoYTeBWSxTwI4z0NoNEonvsOp9vrNiqN2vnjMhW4HGiEVb6+79myiO6cUAbHV2YLiIqeNtI054mUjcEB5UuT1YY+1QUbdlByfzJmWlia+JhovCmxTcvDZh/zsp5e58EdfZ7Po5K3CWKP91vKQK09fsJXnpNkqYlIW0zFlU5NZS5Gt8PQ/+Xs01wpGP7+FG2eoS49jLl1FffQBicpIB6uYzSuY1U1YG3Zr56wkGEoDtVIYpVC4gOijPWPifKtoAkQ0W5KSpil1qKWKwbEoDRvkE2/VDV11n0l3i1ZGzrVoqfb1AXQwX9RAE/aSRic5SVagdUJT19hqgY+cxC5P0Y6Fw5MZ7394m+HKkMHqOv3V83iSYjo7KY5uyc9S+K6TvTylSHoUReY5GOva+/8IxYULzHY08xFsNScU6QJyhb5ymWrtHCZJKLY3kEpQq3vwxnf/Son8jThtBNrgRatYYxgjaD1RPosB3pmzCrANuqlIjOdrUAFdIs6XOHj9+LlNF/SVi5vZCePRlJODPeziGH3hHHmRka2uomSASlKcMggehdFIFXqeO9K+Id3M6G1d4JrRPH9yzAdv/oYfv/ke80JRLirEOkxifEszpfjkw5s8ce0y59fXl9fWKVur4zHp/qaisWQ82Z5qrJ8To/BtsrtOPRL4U4DWaG4P7M/d629/eSNC4Q+AUB3Vym7ZWAeFMwS28FA2Y11bC41zYDSSJO3hctpUjffz5I7x2XQwEFs3O3gaGl93rpVv3Wi0CizywfjEO5eNE+rGtXWitrGnMsi+rKbhwSef8tOf/YLv/M7rDFJDjG9rF0MO0jqx0TAS5dqousPDO532xpe2IbqsBKMdupqjUxMyJF4d+Udx7Yb28HPgv0zUhohAaB9HxfsuPZ42KB3In+lauxKcJ2YzslDj7w+vBGdtCGgtfX7c70SjJcCxg2znkymjoyPy4ZCVlVWu3LjGlccukhaeod1vE9+FRSuFq2rKuiFNNYvphHpRsntvl/c++JgHh8c0CHXdtIgqozV79+5z/8FD1h+/8bnOKMGhbTdpPDAlOKbx8DR+LNaF9uQKbRK0DR2K4n+qy82osC+Wc+VndRnB17+GTK8KvDptIKdd/6aVWWI8jFI7hbINSvnMo9fNGtf2W1oqRYyrJTxze+iIl3nTOGazkqo63Uo5nr9aQZYaenlOL/GtVhUK62BclkzKBY1tWkmICNI4Dh8+5PbtT1lfXaFf5KQm7c54CFxSUef5r96gSWgjySoibXwZFJlGG4OuHZU78rqMnMX4mAOnmWdTUptCkjCZlRT9ijRJQRuSrEfWnK0c0yQhSzR7D+/x2f17zBczz7Hm4PKlq1y/epWDg0e8c+8+X//q19l98IDRZMY3v/46b7495/27n1JXa4hLELNUmto6eS4gGaPmXj4Tlk6jJcTLfDJhOp97vap0W/YZDyqlDEnWJ896JDrBuYrZfIS11bIt4uVpLW/87Bd8+1vf5trGVndetEMIY1BRw3a6nbwIGdB4xneB5sQYHnvxOslRwb37h2xdWKXIEvbuHjKfz5mNJ1inINOUyvH0i9fob2ru375HbTRF/yxbt8dkjW27X2iVkaS5z2iLwTY1GoNoMCZBnxySLCZIzBwuGfBRVjEY0s4HS4Z+60F20izLmtFoRFt4rSOSIM6dAL58NG1Jkg1JmlKWFWVdhvK/QL4unYZUIhw8eMiff/e7XP3fXqJI8vb8i3wL0aJWtH0+u4UQ9KhHVjtwIVGROGziOTQuXbtKb1xwsL9PPdcsbAZJQja0JMOM0jmkURR5gclzVtfWzlCGy9L0617E2106nMMiGufA1R5hQ+D0I5wxOvEoUZMmXlfZDJE5EkprrAhilOeYwJLEIzHuiNB1tJ0y3QVSY9Kk7TGpgr3rBIdFizDINRc3B2ys9aicY3d/wv5JSWNPnz8OX3787ttv8fhjV/jiF18O6y64Q6qzBboMtiJJEkpRNAtFhcM5GNJQ1CXXnj1Hcu7rlD97Cx4eITsXUJeex3zyiKxvUb0CfekJ1PY6FCswsqijkubhHsx+O3Hm30R2JsyZ0opUaS8rBUoHVJAj0EirFqHmRIVOhpDnGXpae7sVR6MUVoQKL3IjjsQ2YS1H3UiLZIUYQAj6NuxXX8D0VwQGxAcizm0MeebxS1zeWcfWNQ/u7XHzzkMejRc0MSgTNKgTy+TkIe+++Q7Xzn+dlX4W1gnt9vWOYigXx6NDytIy3j+kOTgEWzPUCtNUqLTgmf/Tf8vGP/kmu//hP3H8m/vIzmWs7qNO7tBbWyVRBZpVkrUL6PUVyk/v4+4/pPrsDu7e7TOToZ8SweAwJhJ/+7I7MT4g4xpH0zT+fNKRpTQELV1AyQXjo3E1zhhUkVE2DZPGMVKKuYY1LJebBmPjogl7LkDnlxodhYERSHSXFlzrmwRbUStWV1f58stf4OUXn2NlpY+1cw7v3+bNN97irZu7TErbroGYbHe25M4nb3Pzs8d59amL3t2P8YelM7UNAChHYxv2phMWeycogVzDWpr6+hO9RvrsP+K57/x95u99l0ff+z6zfYO1FQwG9C9dwEiFu3mX5Il19M6AJYP9TC5FbI7hrWBjDNZZamtxLZpIdUZBmG9tFFma+uS3UjSJpk4UtQshTqVIEHLnAuedELloUXHPLdkasRQS8Xy4cmontlf8jMSkrK6vc/HqFbZ3dkizgvFkwYMHj3h07z6L8SGKslsMIjRWODiacvPmbS5fvcLVwRronrfPpdvuKgaJWv3gbeTECaaqA2l6RSrO82pRs/Ola9RPv8KdHz9L+e5N7PEdsvUK/eTjKOmhJiOS9VWkgmJt+Fvl8dcO2rjg/CC6m8AWKu+d3tZIEe/UWW2Q4Tp6PoHRfmuMOtcgVqOaxu+sWMhGZywopK3TXSxqRocnTI8foJopxiimJ8fkvVWKLMc7NpGt3C+mumpIcs8GnaYGowxkPovW2zjH8699hUoMP3nzPRYKX7OXmLYW8/johL2DA86trnG6Q4Cc0gNxQanw7EpiuYgn3Y0QS42CSKJEF5QxOhiNgWBNoI26n7WvGGGBOkmCo+sNGBHvKKhODEHZCpQl8/kcW/tFKAhD5f/mRe9l733lZWeaEARt3Ub/W6V8Fly8Qji/ucqrL9yg6GWsrgzo5Tl5nmNMF+WsqoqqrJjPZhwdjzg5GnP/4JiTRU0VSPeWFYc0lo/f+5Ar1y7z4o3HSPGw5jag0YnSGzziyy46ypugjox3IJ3zhpUoTxytrGCUQWGQ0DgyHvmt8QTIfxEZxmq9OPioRGijcK0voFTIxPp5lCCTmGVwVnCzKalSqMS3t21q8YEcPj90ae8fFWptLaOjY6aHj7DVhKoa0hv00UmKSILDtJQXgg+8KWN8pjZR6CxjmG6hlGJ1c4v1rS1+9Ytf88nuQ4wxpGlKLBOyVc2nn9ziyceuUpikQ6gEhzWSmC+P25/J3vlV1kETD4YgHydI3QRy3fhGhVpiHtbSOcxneXkDRnkjzUgbpzA6lJJpH8zBSWuId6wDmlMQuaBXVcyYtzPe5nr9fCwf6sp30KqrisVsjrM1JjidwQ8nNQmbK32ee/IaTz3zJOd2zlP0+oiDxXjOrZu3+NU773N/b49ZWWJtJO2Gerbg0b0HHFy+xPrKKlk/CdTRXiafNzBaIFDQ+1Z87bEJ+1OUxrlwdpQ1VlVsbmyzNlwnL3psb51na/UcVIrUNhweHzDNTih6fe/4Onyx9VleVtjausS1y1e4fe8uxqRkScZ4uuDpZy5hTEJZNvRW1nDO0TQVjw4f8f6tD7AoUpMxLFZIje8K0YjvoKha907A2rCn1akp8+AWaZ1uCQ7a8eEhViKbTBS9P22y3ipPvfAyr7zyRR6/cpE8TRiNRvzqJ7/gJz/5C0azUQhahnsoON7b590PP+DqV78WP6xVMEsqpxsXypcg6q7c1UVvRAQxnstkfb1Ar62TDRwqd/R6ho1za9y+aZnPGobba6hGmJ7M2b9/l40qYZgLyjiMOjtuIqUgzfMwTQpbVzQBop5lGbWtMco7Fo2TrpVt1POn4aXtLCx5gIRDZskIDZ8g3fez6ZzJNJZZ6FZPSQijKBS9POPpG9d54YVnuXj5IoPhACWag4Njfv7TX/HW++8zrebEIF0kEkb5jPM7b77F/e/c4/Grj3PqMAzOhwtncjzF2iCABF0Z9q0LBflSW1Ttea7mswWuFupZjdOGJM8Y0tDUC4QajMGkhs0LfRLTY1YtzkyGEHREXXugS1h3Wgmp1jQGnDI4B2Udyj2XAmJiBQKqTScJOk1orAWtqdKE2mqOG0ulFBnChbpiGMoYY4Y/znV7WsSpl6UAGN2ZFBFwSsFGT/P1L13lmZe+yHBjG+fgcP+AX/3yXX717icsygZC2ABAnGM2n/Lmm7/m6aefoj8ccMozVXG9+d9pFLZ2TMo5zGYkwJr2ROouT6nWLvDMP/w/oP74Qw5/9L8wuruP0aD7Qu/cAHsoJAcHmAvnqYpV7N597Mkd9O6nUM/PToZOoGkw0VHSvgU0WjBaUzc11jZoU3i7TARxDhtK/iwmlNc4RCe4ImeRzjmuG46VUCvFtgjDRUUa9l6cs+h8Rx8m7nGJNlbUs8TpDXLXiu3Ndf7g97/Bk08/iUkLRBzPLSY8+f47/PkPfsaneyOiwL0Od9im4u4nH3N/91meeeIip6yMVql2NrNYS9LUaFciOBKlyXWGLgY0JudXt3NefPV/wxNf/D3K33yPg3dvMVkIXNhm5fFrNLfv4m7eQe+co24MNktgVSErDqPOdi9qpRj2csRaH0AJJO9GYKK8r0NITHXAIo0T500irTyBsXib0ZoUpQxTaXggjikasYpVp5FF09pufg51UL/dqbRsvQmtS3bKToyiz3s9Xn39K3z9K6/S7/VatO9g9SLr5y+T5n/Gz39zm3ndoUeizTIfHfDxJ3d48fGLDNIlWwfV3bx9WiFZLFgTYdV42ScxWV0MaMyU7/35D/johVf40vP/hMee/gb13ZscfnKXR3WJWj+PPPqE+uQQmprm7kOkPsNzEYhkIEZrz2ejU6yzNM4ihuAXL9t0oMQ3qFDiaGqLShPqJGGB5sTBFG/zbhmDqRty6VqexOS3/7mTWoc/VJ57r53T7vfxM4zWbG9t8MJLz/LsF55jY2sbY1LmZcP9hye8/c7H3HznXaZHDxAWwUbx91nUDbt7h9z+9BY7V66RxeTQXzL/VZu8RsDWlvmipJrOgJocIUsUJk9QqWaw3nD+9Stsf/FpHr35MpOfv0d18CmD/iqMZh6VVPRxkxHGTn+rTP4GxW9qyWMLA1atWmuVTDvF1qERtG0wg1VkPkZsFfzMcLDUVcjg6O7jgyERyaamkzl79z9DqilpojyJUF1jqxN0usf5nSuBsVlawQuOqirRaYjAh9aM7TbTimy4wgtffpW79/f56P5noEMrtcBMPR1PuP9gl+vXrtFLPVlSRAS144+KVTliBZwSAduAraGuIDWemNgKlA2Shqx0mEsTD9hQiuADKPHvZ30pRClMmoJKvGETNIlrnf121/isbGMpyorKWSpraVD0jEJr558zOgXBa44OtDc9u88CD4mrq5qDgz2yLGFzY4vr16/y2LULHsaWpJHs3QcUxKG0YjadkmSJ7/zkLDSeLPON37zLrb1jTJoync4wSYLR+Ow6wsfv3+TxSzusF0WLHG6frd3o0gU9wtxH1oC4KdtDQPDwd1sjjUcmeFSUii+iLRlk2RE9y2sJdg20rY1UG8bpDAq0Jwprx+bRFYIiWV2FtXXcbAxicdaTxWmloPHlUr7+YblGPx56wmw64eDRLtVigrYlyjmq0QlHuw/YOH8RpduV5UftHOWihDxHGU2WpJ6cLnCUpHmPi1ev8iURDv7sB4zKkuX6YYBH93bZPzjg4vY2iDkdrGtnJ+qgGKTSSG2x8zmmLoOh4NuZ102DXUxgOGiDet5B0a1u83I8hQM8kyuWAGql0eIREYnyLRYN2qOJlApdsLxxiAGLpRGH0rEbn197yjWopvalU+JoW2lA9PDDt9JmD3GCqxukqVktUgrTi2qdJE1Y31jh+S88zQsvvkhWDFHaBzE1MOit88zaFr2NTX758ze4c/8BJ9Np4L3wkhgdjzjaP2SydY7V/rAlghToupMpzxWl4nKrGm982LDfgpMotgbVsLLa58rKZeZ1SdEbsDbcJk0LEj2gdj7Qt3XxEraxLBYLaqVI0rw9Y87yKvo9Miccj47ZWtsGfYxqLBvrA0ajIzZ2dki0oj/oUZcLjmcThoMVbJ3QV0PWBuvsrG7gSkudO2KxW7QixAlSBnLLNs8bHXnoFr9qbZfDvQP/yhjUE++0mWLAt//hP+CPvvk11oqi3Ts7F85x4+oVLl84x//0r/81x7NR+7nKZxP4za/f5Fuvf4WeScLd/Jrzq6xVkCHwH8tD7BIJoF/HSgSpSnRV0s8yXOZwOwbVO4cWONg9Ik96FJuObGiYHswYriVkvQbjEnRpmUwbyvLsOtaoUMIVA6QmyTCiSVOfwDFO40InSqlLz3kT0qgSzrW2PKm2qMaGFO+yoR7tpfBdsFW8H+e/n4ymzGdTHzxQjshxFcd4fmuD//of/RFffO118t4wICi9JJ4QeOnLX+LH3/shf/Lv/iP74yNP5K86Q1gpxeHBMe++9x7Xr9wg9t7ryr2hO1V8y9J4aErtcCGYpcVhxATCdk9Qe3x0zO7uA0wvo5wLSjfUZYmlYbCWY9KE3goYVVDNatJ+wWNbj52ZDAlDzQRvM6SGsqo9kk8rUq2wiX/ixtbeJY/lfjFTHtsNI8FUqyHPqZKMA+bs4WhEsakSXOkDRG3pSyfo1hlcdsBVJLxTS7ZBeHWWaF598TJf/va3yVYeA+dLfS6uXuXvXHiSOvlf+OUbb2GtXTrXfbvo3Qf32b3/GY8/9UwopY32VufyxF/l0zmXdEi2aU0p4EyCLQb86uYhP95/k2/+zqs8+0+/zIXRpxx99Am7b9TYzctUu7swPWA+qagPR6RzwQw3cPn+mapUrRQreYGrGh+gFyFNMpCGRGnvUwRUKiHw5TTedla+W5J1QuMarFYorVjUlrF1zACnHInRMJmhQ+lf1LcB0BNkdprJLvIBto7jUmKkSHN+5xtf4YnnvoAxWdtF1hSbXH/xNb4mioN/+11OKhvyta2bz3h0wO3bd3nyxg5GL5WynrrCilqU5FVNpjXKJDRpygJDrg1ONfzZv/xn/Ol/fJJX/s7f4Xe/+t/w+Csz+Owd7v30VxxVjunRBNPUNPtHVB/eIcGSnsuw2lsRZ32lygQn01dbmNwnO1OtUE7jGvFJvFCCo1RExgRidXFoB7asYcVQ0dDUJX0MC2epRZE5h57NfGAo6GNBAgIr6OQ4hcHu8JjdQJMQ9HfUAdqkXL76BC+9/DK9Xp+IAPEBpoS1rWu8+tWvc//+AZ88GLfcqMFlxdUVu3dvczB9md56n7/cqzJy4oDYBjc6IBdHYhJUklGahEoZMmPQsuDDv/gf+d6f/Ud+9Op3+Na3vs3rL/weVx/fZ+OJX3Pvg084fO+O96omj1B39lHzswy+hUA+wmDQR1RoOW5DElTRAgs68KgvNyQEX+tFhclzSguzWji2MFFC6oTtNPVk4c61/kxUnG0XwKWxxD97oE0ARBCaIsT9qKDf7/HUM9d4+UvPcf7CRcCAU/SShJXrBUWRUdcNH741p5zuIYGmOsp6NF3w2Z0HvDQZkQ02QZluDcWhLOlZaRy6rOjbhgyHaMgSQ2o0Ks9osj5vvXWTC/uHPPXSl3nmD65x8Pw2h+8/AdMJ2b2HDPoLeufOM/7oEDX57Yzgf82gjTez/Xg7GKFfrNJlqdsMnP+FVoKqFxhpaNLMGyM6Hl4gdeWDJMq00c+QGMJaR1XXVCcHyHwPpYS68u2KseBczfzoAfXmOnk+DD52aFdNLCXQftK1bgnGlAIJmfbe+iovvPwF7uw9pHI+1CDRIVKao0cHHO4fsrG2htaaNEs814SolpF8ia7Im0ACtlxg6xIjFuUUKIuzjqaq0b3CG1RavIIXD72sxWJD/23/KGfuJobyGEjaQJGE7lGcMt4gtOkTcOUCmU0xVc3AGLRRaBKsDiU2ArHON0LhfSRy2YDxwh2PJ4we3aeeH+OGq9jVDVSS+sJ4rduOARGqasVnNo3WJEmGMqHUSSkuPvkEq9tb8N0f8uGDfUyiyfPCR4aVprYND+/vsncwYuVSgdIRnu9d+kiW25VmBrhf9EGcw9Y1uqpQqYYsQYmGusHKHOn3QZnQSWz5SX2pCtLV/Z/9Jd2BE7+0frlaGo3PWnRjUB7a7gSamjxLqYseri7930LQxs3nrbPXBjqXrMB6Mefg7qc01ZQkTYjlEdiG+fEexWBIf7gSBhWYGQRS7eGwHpKedLEkpRBlIFNsXdzh6sULvHfrTtfNJDgdtm648+kd+mlCkfjygKzfDzpjaa4jTJZQ11+XJE3ZZtOUC+VuzuHqBtfyAHzegfmrf/pbX14w0Y/r0D/eD2v3vwpBM6VDG9PEd0dx4utoJdT/R0iw1A1SVW3g9NQVjZgoq7BHh4OCG9cvsbGagNStHjBJQn845PzlS6TFqtfR6FMlACbNuXDpClevHjCezJktSmzj2tst5gtOjk+YTCc0rsGYPC5VHC6gEaOe8FxZrpxDXUKoB47BUpcY38EtGaCbhHLegNIkiWWr3yMv+mSJz8gVgz4rGxs0R/vYxqJUQyxBO8srz3OOjva4/egOJ+MpRnmdvt0rWB0U3Ll7l3E9J7GrPDwaceX6U1w6d45hnvLub97k0vY1clOQKu0RaG0XkYCUcS7IU2jLXYIFsWSSnvpu0WjWt69QS4O1FlvXCIprzzzJH/7uV9ko8iC/LvCTFhlf//bv8PDePf7TD75HFQlnw3+P7t7jcDzi0vomsZDW21XtgerHHfaQiCCLBbZpsE2AWAcEj2sabG15dHyAXa9ZVDVS7pNojaFhY3ODsp5jZ3MoLdNFxexYM7ycg8rQfUOanSHhogih0yzoNGzNFKVSn3TKEk8qrQy9Xo+manB5hlUqlNq6wCOjfbea2QxW/TzEf08H2MLMRuRU2O+j0QScI8/8GCLaE4SiSPl7f/8P+Mo3v4NSsd152DnhY4tBn2/+4d9h3gj/6k/+NfNyGpWrZwdUCqzj3Xfe4/e+/Xv00mLpc3wZunahGYIERzUgDKSsoaxDpx1vI2EVoh2uyHBKU04se/c+oUhSUmPoDxNKETKdcm51lVk1RynN9tomOxtXKOe/vbXp3+RSKBLjO+yleUpTLXBWMMZ3JCqdn1NXn5aLVtpnb2PmXzy5+2I+R9KCpmpIa0tf4MRZ0iQlKS1qsQi5HukcjWhHtF0Ql5wZBKO7rG48Gi+sFzz/8sukw6tAsrRgFP3hGq+9/jVu393l0cOHrWOhwmfOFwvu3v2M648/2ZWTh8Pfx/wCksgJk5MTMrGkSiFJ6vkIkwKjFOODfX7xZ/8P3vrRf+LF3/27fPPbr/Psq0/wzJNP8ejdD9l/7wDTE2T/Ifre+/TWM9SVi9SJ6co9zugyoVzN4YM2/UGfcjZDK0VtG9/t1XkOmeg0Oue5zZTzNpezNRYBrWmaioE2lCKUTshE4HiEci7wT7HEyQat4RpNK+XR8Lnp7DoVEXBKcfniBZ59/gW0yToEaUAH67THjee+wPW33+Otm/docRAiIA7bzLl/9y5V9SXSwrRna7em47+Cnc59NxxnffMSa9BpFhaxo3j0Ebsfvsl/uPlTfv69b/L1v/t7fOerX+Xqf/0sO5+9zZ2jtyh1htYT3MFN8o1t0p3nqR8+Qhe9M5WhH7d3rD3XnufZUiEgg3NYS0ik+6+eekAFJKMhlobZqkIVfSTzPDc9pejXltooctcgJ8dQ10ieh+0X0vaBbzF24fR2hJAaw/n1PkWahKRh9F00SdrnxeeeZWNl6AOBEotdo65O2Dh/jaeevs79g3eYVbb1Wfy9LfPjQ44PJ+ysFS1KPFakRH3hdXHDfP+QqhZy5YPkIr4Kwjekadja22N28DG3Ht3izhs/5Cev/wHf+tbXeemJb/PU4y8xu77O/hvvU9p1Fr0SlZ/duRimEkRYWxlSO4cxxtMLuEjO7pCWyN3Pb5wrJYqqLBn0MybBl8iVYi6OVEEq4MYzqEpiAPZ0QBROuR3K+5cYTZL1SPMVlNRLlrlfPzsXt3niyRtsb51DkXQUZkrIjOLKuTWee+Y6+7sP2Z2PoC3t9//XtePgcMzR/j7r56+i1RL52jJIhbAz6xo9nTG0FSsKJEmoEkOtDUanNCrnzXceMvruz3jh12/y0itf5NozL7P6jasspinJRzvIbETlLPNZxtCcYXlUCwRr8Uuth9S9IvxKKeVZ75UCZTwHCqCSFJ0loH0LLKrSozV02t4nkvCKbcBVbJzbplmMaKoxWZbQX1lDnOHk6CH9lT5FlqFM0hr34awiSUIZQezipKKxFIn7vEm0ff4c/bygnkxxOjp1UItj/2Cf2ckD1nOoGijzlP7KGqk2bRmUhrbu1kUPpG58u2/xh7m2gjQWV1WgAx+In0y/sbXBOhCJuAiBpeV4NtcScsAYnEog8OvESGLn7IeotBJsv4dkGczn+BbbAZHggIXPAjskBLOC7E7HEqjqmuP9A8ZHj9BSkyWGejbicH+X9e0LKJ3gq/EjP5Jgbc2snNPv90mzHGNOZwR0krO6fZ6vfOUVHv2H7zJuAvcO0mbAbFVx584dzm+ueiUdAkBqOcwi3ThbR0iBchYJCgUIpLvWE+7WHlItRsJBHl3k+Al+pi2tr3W2l0TV4UcdCbCjGxZLLj0njAmB1hB09Uea7+xU1SiVoE1QRsFgqqcTtPKoNkcwjoLBWC4qjFGsrAyYHHljKl9ZIUlSJuNjkiInzUxgX29Xlw8I5SmiQiekwJHVAtbC06R5zvb2NnzyqYdXhgyiKLBzx2w2JUsVWaIpJ3OcE9JB3xOlqUAdJoSaW/+DXcwxtgYV2ps3rr1pg0KMQtWOZU716OwIcoqs7iwuFQKo0bD2DoP/izfCCETEIWhjlJdXaE3uEB+0WUKuIEDjgzZIOEzjYbgUzDp1aUUx6HHh8g7b59fCUCT8yWDSlKzXDwEbTun8eA4Ugz7nLlxg7cFD9o6OKJuq1TR1XTOfzVjM51hrW5/klLKJnyseiWFnM1TlgzZxlTsFVhsWjWO2qCmt7/glDqy1NM4FUsEQQlearOhj0py6KWlsjTHpmTsYTiyTcsr+8RGzeYVWkJqU3d2HaKVZGw5Y39rkSy+9SGEbVF6ws7HJb375Mz64d4svvPQSOYrj8QgZrnaoKkLgWwS1KLFL3Qs7YUcfYzmYA9/847/DV/7wdyHwwFnnv2ZFwWq/H+ZeiNnF+LFJlvHlV1/hp7/8JXvTYyJnhFKK6fGIh/v7XF7bXGJLoiu3i+d+/MYJajIDa7u9KxG4YZBej6sbl3j7o18zdhUrmwXoivnxlEePTiiKBGUrpLGkWpPlGZXJSZTCSHWmSjUSZUpE2RrfwUQZHThMFGmaoXAolVDXDQz7AWEZSBZDpF+7BjcZLyuRJeto6Xfh7y2xOLCzs87//p/+fZLUO64mBru1ptfPufHsU2idts5IDFJLuIlCYZKE177yKr/4xRvcvPUBgg3thLute/+zu5wcH9I/f7mzmYTWsYiWRzwbEYeqFqh6EXSPf60Jj2OzjLIsybXi8oXL2MoxK0/QqcFNK/qDPhtrFxgf3EZYsHF+E9sojuaTM5Nhe4nXmyoWYopPgiVZwWK+CBwZDbFLng/SAM5hxKM+tXiusXLR4FYLbKLpGXCiaWxDXzmYTVFHR6ida2E7hWRVzPi4QMofuJwurue8fGMN46Ij50s4jNFcvrTN5s4NT+r7lyLtsLOzw/XrT/Bobw+ci/EcBM8Rt/fgAU21wCTehlb47RGRBFoBtcMdjmhcTaNAN47CObSqoEhZHc94cu8Be8f3+PFn7/H2T77Ml37nD/jdr7/MU79zna0nLjH58G1G+yWzoqTqreGSc2A/I7StOjsRatWCRJWP9vqzXCuaxvqAIUuJDlQIuvoomdEGaQSHwQwG9NKcXCmMs1gUA2vh8BjmC2QlBCtcRPv7z2iDz6EE4uXnHuf6dooy3nZBaLk5189fodfr/yU9DH7PFb0Vnn72aT64tcusipxTMbHhOD7aZzpb0M8HoRV99wHLKDm7v++RwiH5oK0lcRVGa5yGiw8POD+f8aiacn90hz9594f84qVv85U/+F1+99Xf5en//gXqO+9x8OlD5hsFx1WP5vYc88kR6uwq3MK4FY0Dh+/aqTC+okK7YBc7GrE0QCKxFTst6saF8xwRprMpTbKNHgxZT3wzkJXMYJViVRqSw0PUtMQNsm7SUcSct7iIRPd/unD5Mf7BP/6HiPNlP/E8VErjSOivbHuibhWqD6KeDrpRJTmXLl+ll3/AtFwu7fHf1YsZx8cjrNvGGI9E7Sz1DgGtqjl678g3IFDKO+TioK7RaYZzlvWjMSvHM05Kx735G7z38GM+/c2PePb17/D1b7zGS8/8Y64+fcDkzk0++8271Gdt34SnytKE6dzb/T4I7EtifbcoL7tIQ0TrZyfMZzNWh1s0eUovMSjnbbZCKQrnkPEUNR3BRuOrUdrZpDvb2h8UTjm2t9d54pkbVLNtFE1n2ooPrl67ssXOxcskSb60GaNXq+hlCZfObXBh5wL7u/do5jOUom1G5BCmi4qHjx7x+LOeSry1m9sBqdZndlVDNRphmgorghGNOO/TK2V8R8rjOYcfH/PDew/45INbPPvCOzz3xS9x+annufT8JWazdU4eTHFXrmOrld8qj79ZeVQ7+OVlKp0xLPG419jGMa0tGYpMa0zRh2oOJvOR3abBVA2u8kRa7acFQybJEnTqjfDty9dAStK8Hwynmnw4JMkHYPJo2rdjEfFGqFIap1wgRwbwdbB+bQlKC8Wgx/rqKscnI5raUotrDwaUZf3cOVY2NqEWptMRrlxQmQwTCHO9buncTuUsejHzWSsRaBqc8pwZTe3AJFhbIy3ExRPw2taRjFwdf30J/f8Xof9QYz6fIfEGx/JxAQqLJe31UcMhdjbpAnEEY6CpaTMLAbLi2lv54NRitmDvwV3q+cijMpxQNTXWWkb7uyR5j5WVlUjU0klSecJFv/h9RiwqERWVBbB1+RI3HrvCWx/d9t2tVFcGoZTi4b17HF7eZGt1naZqSPoF/f4wuOSRIFC1gY424NTUqNpnA5XDO/qhs5E42xq1Lq5/gRhsa7+LdY9neamOTPi0tCB2aonObrBrPGJFB8Uqgm1qbCh1SPoFzcKhjcFkKVhhPp6QifOIGBeCK+F8ywtPYrl24RLFYEha9DBZhrOOfLhJ2uv51tPQOeNhIF3grSPnbD2cAInUxjBYW8MkCc18gTQd03ujFE4MxcomqQKdemRVvVgwr0v6q0OvJMO8uBB8Yz4L6BrlS7+UxYlG8gLyPCAc/DruGrhLCHap/zJbMQSZutbfQWCRsV/Hrhj+7zp2OnGCE43KUpz2td3tXNc1sliAs0jYN9LqlGU95TeaVrGtZoZSw3Bf1b1G/NqJjuLpY9R/r41hdXOD4coKWZbBvH0Bzvoy1boqcS4SsMcyUr8W23GJoBy46Qxdlp7ELrzDKagVLOqaRalopMEYRWIUOEe1KFnkFUmSkwaOMJ2kaJPiqgVO6jNH2QCcjMdYYD6vcNZ5gzoJWXZTk28ISVrw2We3eO/d91g/f44Xrl/nZD5B64TdRwcUOiHvDxGT4ExC3NoKfPvf+dS3SE1M63nHeuoYCAg2LwBZnpIHLjd0dF9VG6SJCeVOV3TOxs61y1y7uMP+zeOoUHAITV1x/8EuX3rqmXYNtRxZhPsLIRAbzsDxyCNRwhmJc6A8EsLi+OT99/n0wS5JP/UEosMe1AmDDYNalGSZonIpk0XNyaRh61Kf6dERB4cnPnByVpcinBkOcQ3apJ641HpCTAFMopeyxBZWhjTi88GqRRrhycHHo9ZxOLXfCMKK7+H03y5eucDFy+fbQFn8P0a1XTSSgxDbcg4VEcRe62+d2+SLL73ErTsfUzUWS9hn4e+jkwmHRwfsnL/U7enocEbZ0pUXIKBGc8yiI5z1sHlAa8ok4/B4zGi2wI5rrKmg16BJ0dqjkh+d7IJ2TKs5H9z/AE3GweHx2ckwDNQTZ0biSJ9cEu0JhuPs2ybgHZy3/9D41tLKczEZPMKxnEwx11IGgxV6swkrCJsmI0dI64b63n30cy+1nx33bKs3nSN223r66cd46rF/hFauDSgpF+RrEkyxTnTmInInotzSJOHq9Rv8/Fe/7DpZCSHQCOOTE+rFlKwXeG3afellaJSC8ZSV4z1ShMyERiBiseKQYUFyOOWxkzGXEmGvrHi0+HN+/Nk7vP2j53jx69/mK1/9Ms994wusT3e5/847fPSTdxn95Hskn31MWddnKsXGNR6ZF5KiXh+GUIb1AeiYkIlITa262dfaUJcVM+tY3dogV59ixDHwPbxJpYLxEfbwCIbB74i2BzEA1/FpohTXn/8i8GIQUbB1W5hcRHCGudeRHMC/VjCcu3yNosiZVdNTtoQCqsWM8XjEuY1BCHC3h3a7/7WzuLv3WXMeOa2NwYnvOMfKgJmrsMwoqilXbMlqXXFQv8/hz+7zbz76IT9/7mt87Xd/l9e//DrXvjGn2PkFH333u9z95Y+pPt3DVn9FtPBvcYkItQtlSNJRL8TGoCIOGzg8lY7UGIIH93VBOWsbjk+mTORJ1s9vYW7th1Ly4CNoTTU6IDk8ge2VNpHUETlL+9mx3M5kK6ydX/VrJdjD8X7iM5StPuyKHbxdJAgYzcb2eYaDgoNx18nQO/FC1VQcjceeJ0tH+oTwChUTdcDRAf2TEYQqzUQrxPpScLe+wljPOdI1iCWbz7nelGw1JYc3f8r79z/i01/9gF+8+g1e++rrvPzk13ju8hV6/5f/29kJcckM9HtNOhnazldsyxV14LEj6DCtmI1GJNsXMMMhvZOUvisptCJVip4IzbzEHeyjdh6DNCOiq+IVPysifkSEG1e2OLfe94GjtsheRVEzHOSsDnu0NTBq6awM4h2uFJw7v0Va9Kjmy2kov6fLquHw6Aik9r6ABI65z/lyCoFZiRyNMDRePyhFYoUEi6QJYnLWx2OenDnGteXg5h4/enDMxx/e5pnn3+bZl1/i0tNfYPDMFYbDl6n3J/B//atF8tdH2siS0Sud26Vk2QHzChbxhJFNXWJRVKMT0tWVEFTxcE2VpGjnoCqRQYzOxcM2KK7gNOligKLvGfyVQlKNTrK2fVrnQkQjKkQJvVXjBRgMnQhwiu6Y0YYs7/lsrXWBdd5/Wl1VOBLEZCgl9M06Ds34eIxox8r6qkeHxEUhoK31LbIRTyKJeBJNMdgkpTEpIr67h1diCm2WCJZihua/gKsYJZQkaXSVT6GjCB0iIk+L7/DiWd6lKHyplFP+eRTYxQJlfTmZz6Z2u8OJMF/M0a7CNHPPb2AtaZqQ91eYjccoJZhmgWE1cK2E9yqFMpo0SdEixK5HFuf5i4iHOWiV8MT167z1/ieeEjgiM8KHjUcTirzHYDCAwjKdHFEbBSYnTZLAjxk9H78uJGQXtfOcPRLWBiJYZSDNcSY4KBKCAURHKiKNJPDznN7oZyxMf1cVnTDV3ZslTYVDnGtl3FQltQDVAp342ksElEnQRqNri6tK6CV0H6M6R00cJBnF+jY+uCGep2a40mZI/CikfS8hHOedGdc5JSFg08ahxHMuxbr108/r34s4UL4lpALsosIupjDs+1btoUxPoaB2MJ74Fr3OcwFgvI6p6hpJM2oX+SkElPOKXvxznq05010+YEMoHfHGmO8YEzvsJRidYJQLmWEvY1v7jCF57tvw1h45psRBWeImY5RtEJIWfRU7aLX3XprSts4+iMgE/oW25JFgyIb3dZL1l0PIegVFf0Ca5cQ94F/mcLbB2cZniQUflBICAb2cQhtoJzDyQRsdcK0WoTGa0gi18eg2H/CBIk9RAuViztSkoAxF3vP8B8qjzJzz8Hml7Zlr1LquORiPqaoKbQy9XkG/l6NTQReOCXtsFxk7K1e4M+wz7PV4/+NPWF/bZHPzPF995TVM7RgtFpAYXK+H4JaCsgInAcJrTCeBNqsfv0bphPM4TKgK6fauhCN8ZvTTW1Sk/2Mx6HPl8iV+8/GHPnlBcDys4/jkpDWgVDR2W+dpqYxSQNUWvbcP2FPWknMOSTQnizm7J1Maydi/N2axcNSbjmm1oOinLBYle0cl+cYGzsH61jqPPt3l0cN9GtFtCd6ZXOL3YcclA4t5yXQ6JtEeFWFt4+v5neCaClb62DxFpgKxlA1QjcUcHYbkhjmdzFmyRlteKejmrHW4o65U7fi8hFUbeHPxnq3RA7EJgNaKG089QZH3qW3VoaGCXVHXDdPQEn6Jyq274+eDEALq4ADdLMLY4yr0pdWTRPNw9Iij8githI3tnN7GCs3cE1Lu7FwlJUEZy+HoiKZWHI/2ubR14cxEGC/ronUXzmIFEmwGjUYEXGPb814p5Z0kkVCS7Z+pEmF0fESlHcnmOmr3Hply3mgO6Ef76V2yck6T9ehmZElDirdznRJMkqPTix4FFPcgrXjbgLK0GzPYwSFAsH3hPDorUItp+56Itp3OFyzKOf34h5CgcdIlMN2jY3qzE1IlofzLk927IiHdWkWVY/b6Daa2bDYNa7OKw6Zhvxrxi4cf8f7Pn+OFr3yTb37rdW585TtsX9vhs7V/xd1//yvk9tkFUAXx3D1EpglO8XM5Z70PEOdHK9qomfXk0s5aqsWC/aMxK9tbOO0wjQugK43LU2Q+Qt17iLq0g6ev83fyhXFBliKe0BjarHl7UsWS86BDHV0ZjVpCb/iNqVhd3SDLc5Bp0MedWmyamjKUo/vV2nXG8aIU7PEJ6YNdiri8mgqnE2qdkWxtcenGeXh9h4/euE01VQxTx0UWrCxq9g/mHP5yl3//0S/5xV+8zqvf+Ta/++p3eOW/fYHnXn+GW3/yJ3zy5+/Bb6fS+JtJUqx34pVDVOxCa1v70NrI+yhI420cX64UKDScw1rL0fE+e6OS9Z0dSvUBiYNGgVlZQRvBTk/g7gN4/GKw68K00+mvGIiDaNf5UUYUeSwrjJ1DY9BHB76z07aTwuQ9kjT18v68IW4ddl4Gfy6ixFXba8eJ7z5n798jmcwC0l9QTe15mdIMc36HJ774HBv9E9744VtYu8LmWsFKNWX1pGa/OmJy/03e+9OPufXmj3nzK3/A177+ZXR/6yyFGIKYYG1c3yBOsC7sk2hjS2eOOHxyBiWcHJ/gdEOyvYa+n5HhEUUq8UhW3VTUD3YxT81Q6ZAu+NL51C7ILNrja/2UjUEechkuvNb7h5Eyoj1pVfRjw+/DuujlKZvrQ7IsY6KWbP9wWesYj2dYW2OMtOduNLfa761FHU8pTkYMlCVTglFgQlKj7vdZ9DZwqmZL1tieN4yaiv2m5tFil/3dEz786FOefeF9nnvxJS4/+TzptUu/VR5/7aBN7J7jH89bOi7sgLbcaMmGtNYjXFS/YHFvilkZgDGQJj4oYQXVeEb0wDnZRrNUPABVPAy7zSbiqF3XQs+YzhhqDzo8wa4S23ViWiJmVHHcS+9pxB+wbRel8DwtkarWoFJ0I6hyis4diiE+Pu9XmA4lQ2o+D4NtcSc0dYOkKXMbGLkBtG9LqI3CuiUOB61oyWbO8grrMkk8HFxagdGuxChjh2BtQ1YUuKb285+kvhWxMb7cpa5oyjmSZcH4iwaoP2CzRJEUQ7YuXGJ2vEexukpa9NFpTnr0iKzXo+iteQMXThE0KqUgOu94SGUcF0qFVnH+4F7f3iTPUspFiYuQ/DCW6XxOozJMVqBEWMlSRGtmJyPmjWV1c90zoxNbgvugjZ7POpLmugrBAI90aASsSbCN7xzl568rMyNwU7hWIZzd5e0EHeQWgiDtv0uiFp819AeFn9S27E0cSVH4lu61BZWSpAlK+YxEImCnM1QxJHp4fnlIK6P2AAwGjnVCFWDhui2ncXj+XNU6DxJp4EK3N9V6k91TOCetzRPtn5gASZYCa62zkSWsbJ3zkMglWSgUlBVuNEKLbZV20wgiDXXjkLTAWd8s24X3toa3iuM7472owHl+NL/N20Sd10Ut5awKM6Q9pZ04R2NraqOxRQpZio//hmDrYoE7PiapK9ApHrKvw/xFHo3OqWgh5jFwIuJzF5/LKkiE5UeuBlhyJn12UyUpyqT+85ay9OJCIDwGpSUGhX3W2VvTflLctEEdnJDM56QSy/nAGUPV69MkBdYlKBH6Rcrm1g4reY/DoyPmkwmz2YK0P2D73DlUqtFFBouEpm5C5vSMxagV69sDqvmMNCtYGw7p9zIP4109z+T4iE8f3GM/X7C6ukFTNhTFkKeefIpPb37Ie+++zc6lS2wOVlk46/mZUO25IWJRx0foqqZJ8rDY/UP4Ly7EiwO6SEW/Tdq1Y+NOOCU0WTJSCfvFByg2NrdItKFuXHeeI5TzuecYi2ui1SYd0XmM/7CYkx7st2ip1pBSQJYxNXPydch0wdVLQ6ga+oNVnHUspjWpGaBwjB6eoLRifDhhMpmjtMa5ukOBnIkQg25BoVJP0NzXmnNr64zGR/7ZTBKytdYHvAuDXVuDyX7QUx5CrZ3A3buopsKZvLVb4r5Tuit+iImFCNTRQTwR8ikxK0sM7vh/fFvveA74/dSiSoLDsbaxRl70Gc9OUMRSHUKm1LFYzJYCSt0lzoWS37CzgwGaPToktR3fVXxFlRoeWMXD4336qynFmqO3kmDngpAgYvn4ziecv3CZXj9DD0A1Fi0JOkv/0v3/tpc43045NJDxnDbiUS+RyLeJQWntwfWuEbT2pY46nHM1cO/ohL3xCdvbWzil0ThUqumtrnlY/6NHyMMHcPVGe/K2MgmyinaIInTCCftHm4guiJJtTzI+n6wTJagkQycFKhAZx8BCtDHFuRBEDZ8ZzE4RwVmh+ew+2WKE4M/3xlW4xGBW1rj8xFVe+wdP89OnCz67eZs8GWAXDe7RMWZcovQx4/1f8cP/+W1+9bMf8tq3/ohvfuOLPP3fvcyNP/qvKP74f3emMowcIc553dY4S+0sjcM3wvA1GYGrwrUOFMEnqXHM7ILpvftcff46i6KgN6tBKbKNdagWJMcnqE/vYF56FtfLOidfqVYaKAm+p99fsXOhMr6TTic66c5V72V2gXDi1+ArqaVTNYhciUMpr691KDVzEnSA+GYc1cefkI5PfPOEYPss6hIZFKxdvsJT3/hjvvUP/4CPf/BveOeNj2j6m1TVmLufPmD6aM76oKA3PGL37f+Zf/vOj/nZc9/iK7//Lb75+v+RF7/8xzzx7p/Ct//PZyZDBeQGH1TDYx5qZ6mqGuegl6ZUddXOiU/CednrEERx0jCvpiymJ+w/POLG4zvUm32KqfNIJ5PRTE8w5Ry5dQvz6gvIWt+viaVAdfz4ODClaLlEva3aoWFEAr0m0Rfp5NrF3xWNtTTtPbpd6/DdLvMkNJVoAQi0Zy5KcOWU6tYteo0n0LZBr1dKUEVBcfkaV1/+X/G13/+7vPLtP+edX99kbAfUVUl95x6Lz+6ztrHB+iBld/ctfvmvP+S9X36Rg+PZmckwPCw4wVpfitnqNOnWsUgoZcOhnG7LpWonHJ1MmMxOMBfWmWcF2BSUkA4KjxKbLrAPHpBOjlH9jdCJL5AStCLsdKLEVu6RHSqGCILwvN3T+ePtFXyf+LpEa3pZSmoSYin6cqdf64SytljnApl0V1HTmWCCVDXu4QH96YgCn5TUtkGjPTBgfYXhjeusJI9zYjaxH71HUu1yTkqGtebwGG6XB9x7+Cvee+cTXnjhN7z82qu/VRp/7aCNby/aEQ5F9hCFnFJSLgRDRIFVDrW6Ck7hyhJxFm0G3rhvHKoJmeHzCiPeSLFBC1sbI/jxgApwNVG8f/Me79/dI80T+kVGbhKSzFBkKVfPr7G9OgAkcKz4T5HPfZbfiJ5tfl5V3vxUvu1cPAhUaHXWRtpEsEron9/EJAZNAvEgJnClTKfocuL9dmM8p45AM6+QYsCk8jwwPgLrZ1GhglEh4edoDJ994EYAnRgsithIUhE4QEIYUeMPEVtXuDQFozG9AbacoYzG9Pte4VYNer6AwVo7uxGlkxrjjV3n6K9u0ltZD1xGXhEOt64QptS/NyoETeh4FJz6Vlx+fbigQUNCCSUaY1LSLEPmHYQ7DqiN8OONM9EpygpYi1Z1y00EXok7pcA53MnYd+JRtJpBANsITappYkYMvw+Atv1nu/aWlM9ZXSoeEq3mjIdBDN5ELebChgncCDH6EeZf+n1Uf0B1cIhNPUN6mmWIWKhL1NERZvscFkUEY4gIdVMFSGQ3JhHFxzdv8Z+//2MWdYUxiS+3SlOKPOOlF5/hpWef9BipMCEmZjeIOiWUJAHT6ZSqqcNYXStvlSiKIvedFpzCiWN8MiLNU/Jen876CmVFznmOiekEnO6QdtaFVpIGU+QeYYffAyI+eCfKt19mmXj9DC8jYLSvw1Vat9wTfuWHvRjnpeW3CKgZZWlSTdLvw+g4dE/A1+kfHCGjYzjX7+YtEi4uG5h0rkL8p3vOoC07S8X/JnI1tO/xV11bT9Bq2/5H7Uu06p7NiQNfdeJ5hlGBXByMKJoHD0mOD0jqeVh1YZz9HLvap7e5woYxVFXlfWmBLOuzuiK4oyNG4xGT0RF3bn+CoHC2wtUVtWuoxXLWm1EpRW0dvUGftcE6ONsGO04OJhiVYyThiSefYa3nDcqT0RHl7AQSzWQy4jfvj3jxuZfZGGa49TVEAhmq8npJj05gOkV6vW5vx2CeCLil+H4r0LjVXThdgjSCYeNRrYpTgeagU1Z6PYwK7GKRq02gCUS0qg3KOXDal5i4eG75OVGHx+TTcdjvtMEHARa9nI8f3acWx9b2gNm8Ik00x8fHLCYVs7KmGCguPrbB5HCGKVImk4rtSxtU9ZTDh1Mmx2d4Lkrgb8oKbxS6Bp0X5L2B31tKSJQiyXJmMkfEUWLpbWzCvVtAs2xSwJ27mMWMpp+2CaO27ChEaeTUoaeCPaWCERpsFKU6fH60YcSfZQqF07p1AtrXBPknSeI7ZtLdhuicCF377nB6xY9wuLZEpJ3hRUV29x7a1R36Lry+6uXM+wOmoxlWOSZNzXRfIeMSa2r6awV5kTBdHNGoHlfPXeLR3iFrPcXlzbNH2ugQ0FChRTQWlChqa6lsgzFQ2iYEvvzGiWrWk8B7VFVdNUymC3Z3H3Du4iWa7TWSxQxjwS4sqixRjcN++DH60mNYY0LXINqSCOts6zR2ZzNAKGuJdnNwHlUkVFOqJdOIDkhVWlwdEpktab4CJWRpQpJ5RKzvIBUEFOya5mjG7PanOONoVIZ1wsx6Pb59cZt7xQ6r517n9/+7bzB68D73Pv6Y2/eOufvhLe7eusv1C6vc2N6m2N3jwb1f8f1/8T5v/+hFXvnG7/HKqy+Rb185UxkmRmOT0HdHwNYNVeWTY6nWlOXCz42TCJVAQtklCp+4q2qOHu4x/8LTDJ64hv70EZQVdjKFuvRr/O49zKMD9PWLwSal1W/OCrZpMIlq96tCfNICj4r3PnjYO9oEeQb7KtQDxe27KBc0jW0/q0Mga7I0Zdjvhb0XGwssIVvnC8Y3P8Johct7LCxYcdTa0V9fRw3X+LO3Dnn1lS/w+B/+9zz2lY958NGHfPj+Le7v1RzKfTb6a1y4tINL72EffMaDX/4L/uT9H/LLl7/JV7/9dV578b8Bzi5oAz5RZ3QklPaBxapcoEWxMuihXOlLZo1pm644Jy1q3DYV49GI2jYcPHhA/cyX6T1zA/3RLkwXMJ/gtOAyg9m9g7mzj3rxsXaOnUQ0olDVjS9xDWMTBbHpRnxt7GCpjEfHxEYl0e4lBmFEWCwWnkA/KF2BYBf50rXBIDbHiIEbb1ciDpyjuX+X6t4+TdqnTByNOOraUZKwtrmJyw0/euNjvvDyCzzx4v+a7zz1gP3bb/P+b97is09GHM1nNIMNVjdXWd92uIN9xre+z/Rw98zkFz1lifqzbHBWUCbFWRvm1+vQmE73TLV+L9V1w3g658HuXS5fuMxsc530eIGxEz/HiwW6bmB/hDx6hNq8jKQ+kL9cgt3W14UxtedScGdERxum44mKpaV/5QPh32edBJn713Y9rPyeN8Z3So1ncvT/20C7c8h0TnX3HkNXYgPPrXXOJwOylOziGjde2OLJbz7H2194nvf+/EkWdz9m1RyxYkqy0QlmdkgpsDtW3P/Be3x0885vlclfO2jjUIHYLEYVg4KK5Sp05J7eCdPM5yVudQuygjrMW7KYYgZrkCbYyqCnE3TjHajASoM4mM/mpIkmy/K/lOg+vHeXt7///RAg8gpbG02e53zrW6+y+cqXAgcEKFyIpkY1yalSnMZaJtO5h8MRnV7/jHmeYZZaKTZ1w2w6pj/sQ+h+02XDglU0nfhW30lYJ9bh6ppmsYA8ZVF5jg4HHjGBNzAq2xAzdr6coXNazuqKdppG+zKMWNaztKDbqCIeujkHkrJEpxlieriyQhxok5IYKKdjZOscsSOYZ/4P87gUTPBoGkcX8AsDUp0DIVotDcUbJRFREceG0AVEVHBglAr7OjqjXXlQDFbEOnwRobGOdDCgl6Qo7UmsY2jDKkE3DsbjUxF7Zy3ihGZukeGQ0gYkTXRuUG1NLS5wKSEtevfsroDe0aeE1mXXwzwovLMugeOjVUgi0FgagXRtHfb2UTqjmS8w2oAxniV+NoGyRLKivU/TWGbTKf1+nyRJWsNCAZlWUM6optN2/WgFU224O8h57vHrpGlsGRyDcnElqNYWtU3D0eiEumnC1Kv2a5KmrK6t+D2vPNS96BekaebXkbT+p5+DpsEd7KMXM8iSLvgRMmfJxgqz6DBJdFT9CDtE0V9S/2cgweDc+rAGSnSYFe07KAQjRiBSPYVAlDcWGxFqpVHDHpZAAA5QWxhPkf09kq1z1CoeguG+YT/FQF5jHbV1NK4NtfvXae8ApFqTpQmxubAgS3uCFka8mM2ZzWeUkQMqTJjRmjxLybLEQ9edC4ZQ1Al+D2lRqMZRfXqXYjJCN007FlGKZnVIXRQkg75vHY72mR+VYoEkKyh6A5q6xtRzEqM5ODogSzSVa6inE8bzMcqc8WYUsDNhrbeKIuHy5St8du8O2sBsNqPoFwz7Ax7dv8tJljEt54ynMxpds7raYyPf4NlLV7lx8TzzeU2ztYmkCappIjwONRqjjo5he6t1uuMct45gcN7bzn0s7YNQ0x0dE81S4IZuw/jPls8lS+Jjhh0djBav53zJsW75n/yglHOYz+6SLeYtwWTUSaKhXFnjo8922VNzNjc1djZlPllwPGlwAisbBRvnelRlhdU1Kyt9nKtpLKytrpHrHid7Z8ec6RsBJDhnAx+WQYfAR5GnNLYORNCKIs9xwHw+ZfXyReTdNJQidmd1NhpRPbgLj6+cOgMB7+zF8+4vDST808JvxPMiBOP/FIIjGI3tz13kDSX4Vvch6N1F10MQMHRTahNRS46Hh/5Jq0eVgNo7Jt2/j6imHWYMRFQb68zzAbOTkpP5lDQ12GZC0c/J84TUaHKVM3o4RSUnDLM1lGRsrayxtfH4GUmwfTpEqRAEV+ikc6Trpqaaz1gpcowC6yqU6uHxM6EkI8xD1dRMp2PEOvYePMLdeAp14SLm3n2MLXGuhjzBNBXuww9JvvwKamOrc+5jYsQ5TxaP6ng9AkwtIkmteMR1ZvBBljC5Xr5eXzsUJ9M5tq6CqFTrTCoFa6s90jRrM+DRnlN4WS7uPOJw7wFap1QOMg0DJSSFpr56mT974w5/9u6/4dvf/AavvPglnt15lvN3b6Jkzuzeh2TOYOyQSxt9BkwpZyc0B3/Bj//H9/j1T17k8ORss/ta+c6tMRlqbU2zmJEC/V6GVCVa8AEto0KgJcytOBbTE07GIyaV4WA0Z+X6Y8ith2QuOIMmpbaW5GQf+eAWyZWLNEl3tsW4WRtoFnyJrhAcbxUI3j3qJ1EQOXA7gKpfUy4o6YPDEWVlWz6ieBOlFIN+n2Gv4BQ5UtDhSgnzBw8ZffYZpYPK+SToOa0ZakVy7hwH5Yjv/z//7/zoP3+Jr/7+d/jG7zzLE68/yaUb73F+tSZZPKBJ5hTuhAvrBZvZJtP5DMMed3/9/+LfvvsX/OzZr56pDEF5NHPgjRIVgijiyFLN1vYK59f6JCFouXzWqGD/GIRqMWU0nVI/usvR5Av01lZQ049J5j4A5ozyydfJAe4376Aev4QbGGJiWOH5jrI0EMcg1NYyKRsa5+XpnMOKJ0bGwfqgxzALvFNq2fcg7DHH6GTEYl7TYR79pfFnxPr6oH1mvYRNFQ2UJfObNzk+mVGrhLlrMKLY0gk9nVJcvMTh7AHf+x9+yPd+8gpf+fYf8PUvPc31Z3f4xuXH2bkwZOtnv2Rh1hkO+igKHr++TTWf88a7756pFKNOtU6YLUq0sxS9gtp5FKNOIkpbIliq1V/zacliXnP7k1tc/epV0ktbsDhGzWbU0xkpgko0elxib9/BXH0Ct9b31MLdERhajEfdGC2bCKyQNthK+z5N5Os41UBEOmR5bS3zRUlT1y0aWEl3tqXGMBz00brj4FRhPlAhGNg46geHVA92qRKoVIZIgrWWCshXh2xsrTN5eIfHt9b5vW9d5/Hnd7j18ZdpTvZQ1Yj601s8euc3rKsJw0vnOZ4LH+8+/K3y+Bt0j+qk4oMeKhx2XWTMX6qNA9TzOc1WisoH2OkJZtAHqRFbIWjfWns+x83HNCvrLflXYxtcOUenvU5QMUCiYHVtANhACOvvJ05hpWb3/gMWLz7PoBgEQcSm3B0SIY7ZoRiN55yMRv5npVonF4TVtSFpYvwjOcHZhn6/R5JmQAhOxHIqAdM0qP19dF1B4l+jQslVraHKCpraE9Qp8M6K+KCNawTBt/1brnk/y8svTgkthPWyh0vUn8sq1OFQvZ7fuGXl260FCL+4BnEWNZ1iEJp2bRCtSK+oFW12QYKnN5vMeefmbarGtRwesa68n6c8deMSvdQHBRq6DRtDbUtoR8DXx5ZV1e306FiI5+9JktjrQoFzVLMxSVFAgMd5R8QjebSAms1wJ8cetp5oX5cbnsE6C8M+tj3kw/qLQUuhda6X7PUzu+LnRdv+1LUkPCHC5lUo/Inz77+blXM2zm3h3nkHlyQ+GCsNWF+DzGIG4zFs5Z0xKJYiz33r6SUJKKUYDvr085zRdLqkD7w8dh8+5OHRIZfOn/clW7HWtw3vqNYxmUymPNh95B2l9pn9fbI8Y3NrAwetYyXiS/W8gU5UFf7/xRy3v4exDQoTDgA/rMYYZGOTMjqUtPGakK1Uy789+ytG8ZdmUut4CPo15SHZPuisEoMK5OoojdUKGQ48r42t8VkcQS1KZHef5EZFkyeI7vaJSJxLwTkYjabsHZywf3iCdTa6bqFjTcbm5ipXLp2nZ7rjImoLEY8mExHG4wmT6YyqrpaeBkxi6PVyiiz3DpJb4v8Q8WUHoW7cnsyQB7voao4K7AKiwKaGamWA6w9D+UoombSKpnFMZ3MSY0izjP5giJ4rMnGM9bGHsdsKVztUZXHqDAlsgX5R8OLTT/No94BG4NLWJvPJhLsP71JsbdA0jofjR9TzEtXXPPbEUzx+6RrNZMqnd25y5cktbn3yFo/27vDkY8+wcWGbJklJm7Jde6Ys0Q8fwJM38HCAbn4h6IEo386D714XF7yKr6H9XXTXVZAHIhyPRr6t7hJcWKNIswyQQOQfgwsByRMDAAKqXJDc+gRj6+51BFNLG+Zb2yyO96jFMtxaYTabcjx1TEvH6uaaN2x1hrZzeoUhTR0b6zmzkWNv9wRloa7OVo7ONi0XmgrcQVonaJOQKOXJ0EVIkoyyqajLKfXl89R5QbYou4MTISlrkps30defwuosyKgrVztlL0XRLCl16aastSvazk5xLpftLUWbiYxyPjoaUVYlEa0V/6aUIssyhv3Y0rYbhl8P3oHRYSw4wXzwCenkqFto4VmtVkwvX+S4sjjnmC4WZKSkmaE2JUXep6xrqnlJYhI2NzaxZc3Gxhabw60lXXE2l0OoRNCiUY23rYhntDi0LVnPYbNv0DjEuoBw9FlWz1vi7bCT0ZjZfMrB/gGjRcn6cJ109BHK1bjEB0OoLTx4AB9+iH71dc9157cQOlGkaYrfL/DwqOTm7piysR7VWTlq21DbhsvrKa8+c8m3K497C+UdSnzJ8f39I5yrwl6NgXMhNZqLFzYo8l54V+xYGsq/JhUHH93mcHZMaUtqcWzkGWtoeht9elev0v/4Hr/44Z/x4S++z8tf+0O+9nvf4ZVnX+OP/ukTvP7a/5e2P/vS7DjTe7FfROzhm7+ch8qseUYBhRkgSJAgW2SzqdaR5OVzo3VufGP7D/BfYq/lWy/LyzrraEk+sq2jHthqNpskSACFsVDzkJVT5Tx+8x4iwhcR+/sSOuJFy9kbBJFVmfntIWJHvO/zPu/z3OJgZw0tShDGrphjDEmasba0ysd3vqZzsHO646gtaM9akWDRSDSVUsD5mUmmGhX3/AtZBD8nJQIpXSjQ63XIMsXW+jaXXr9IrjTBoIdRAhEEyDwDk5Lev09w8zp2cRqGz9YVLGQYOvat1jx69IKvn2yTaZcD2CzHGoM2msXFOf70o9uEskjqAS91gHBi0GvrL118iuRkcVtIwczcNLV63T1b355eJI8mSTl+9Jz9boc0z8k0qCAgjhQVGVG5cJbmuTGyw8f8/pt/z//n2R0+/fgHvPfDH/Ph+69x619c48b3/5T27hapDujbiFSEGOGYkztrz7h75wsefvHvTnUMhRCUKiVIemTGAZmhkMSlMqVSlVuvvcrs9CSBj/2dCLHxzCTHvg3DmMl6k3OTOTIIONzbZ37+LJS+gU5GHoVoYV0ruAH9/B7iyU3sq2exBVHLVTd9Ad+9NwfbG/ztb+5w0M7QBozJMdYx8YOwzDsffJ/3X1kk8D9fFDgK/TajU7Y3txgkmfuzO5HLNaWlWW8yNzVOqFx+NVRF9PM0Pdxn+8UKx3lGpiExlnIc0gwCanGN8uVLnL8yzs2Nb/ni0//Af3jwFV+8/RO+/+GP+MGb17n6k0tcffcXtA726A2gr0NQMWD51//hd6c6jsZ60C1zmp5hYBmvN4hLhZHLKP63wulkudBV0uuldDsJedqik3YIG2VMDjJz4J0NfJG/n2KX1+HqOqLSQIS17yY23iTAnQf6/QFJv+fAcz9GjinlcrdSKaZcqyNVNIzch1ugz6uTJKHTapHnifts65l0frMsxSET42MUluFCOLa7GO6UYPsph49ekHeOGEhNikCJwAH1ShDMjrMnqnz5V19z+f5zXn3rNS5cf41z759h62CC7ZdH7HYC2uE+hn3m6pOUGiHI0h8dj/8mQ3fLSEtmFO7hFydGwV/R8qANA50TVmrInXWXOlZij4K5n5W9Aer4EFlrDEWqlJRUmw2E8r2jBc3UP7GxZoM4jOglfSzOtaPoB93Y3Oa41aZSqmALgbchFXyUJTkaJKyubTJIkhOULD8JLUxMNH2w6v4uCIIh7c0FOb66KRyAZTsdaB041o4IwY56mW25THco2uyTStcThJASrc3Q2cpd6T+WBKoLsKWnDxfBexFAFmCHwLoWtrhCUKnR39/HBk5PIohjpAzci3N0iExSRKV6QmvA0cQGg4FzGyqeq8f9sl6Prz/9lKNOl+E3rUVKSaVaZWrizyhNTQ4D3OJ5F3NgpDvkNslWu8MgLar8YqRibi1xKaZSLjlYwFiyJEEGkWNw+Y26wG+xAmUsZm+PYNBHViLwSaX0FRYbSPJy2VkG+vl6EvH13C+fEBXm4qd4FOyeYsxO/L1/Iu6P/llI9xo6RoOXn8Bakl4PdWaBTCjk0RGmVnZ6IVI6VlGnjd3ZRDQb2MJpKgxQYTBMJorzWCy1Ro3JsQY7h4cn3iX3dLudDo+fLdEcH6PuHZ+Kue6SFjf5slyzsrzOzu7ef3Fz7rMmJpqMN8dG6EoR3Zz46aHivzGI3R3EwR7CaEyeDjfhLLXYuERaLns9rYIB4jac7yTCxUX+IxwjyMqOkrfCTYFRLmit27uCSuxaS63ADnJsrYKJY0w2QLiOGmSSY3YPUO0OKi4P2y4KoGX0wCz9bp+dzR2WltcYpIl7DlYglKDWqLBwbo6JyQZxtebAzOFgQIEP5Jnm8KhFp9shz7JRxglEUUClXKEUx24uGDucn4W4ONatM/nOPsHhPkqnDOEyIclLIf1qGR2XMCJw+7c1GG0x2pCnPWw5JlCKKC6RJoX1dkySpmSZ68UuRTGI09XRyHTObm8PE0D7sOschzC02l2iapnxRo00NcigRDOuMBE3+MHtN9l/ucnB9ia9do/jXoc0gM3jHcbHz5AGISVOcNDcJoX8nsbIArT5LjhTJPXt42O63Q6B8o4Owj1nFQaUanXiMBqu0UMxVkaUfaM1x0fH6KIY4+ehkIJqvTYEBo0AVwuFIZPSAxNid49odRWhCwbOcLUiq5Q4nhwj7kTUE8XOzg45klKjzuTZOugMHSsmZ2Z5/nIfJMg4Z362hOynrB12aPUyr/92mofxorEGYw1REPmFRHhg2K9lxhAIQZ4lJGNTZI1xokGLgrlmrUEYCJ69QH3YRde8wPvJU514HgVYc3jU5rjdGwrIAi659+Kc1WqJM9NTo3Gz7lxugEfvs9tGBZsb26TpYISzuLMhLNTrVZrNBlqb0e7kNZGMNagoQPj1XnQHxHe/ReU99ylC+r3AkkSK1twCm0ub6NBQmyghpWF6eoJeqwOJs2qfHp+GWFIfqxIGVUJVYWJsjkjFpzqC0ldRZRAQIslF5juEBXEgCKuKa+fmuXL5JnFURarAz1s9bM0WgaRSjpmolQima8Sh5XB3k4nL5xl8/ZVj14QB4SDH5hnkGfqLrxEXLsH0DAgHHgV+vhR7/97uHr/7X/6KdNB2OnnGYo0GadmebHJ55l8wMz0DuNapwu1LAwfdjPWVNYR2QG7hriqAailkceGMd1EsHL98PKeh/WKLrReP6GQDAqOJg4BcSHqBonFpnmTyAjfOvcfSbpeNpYd8+pfrfPnJf+bV937KL37xY968+qfM3cxpHe3xbP2IBxt99jqaXpJizARiPgb7q1McRUG5FGMQdI3X15MBU7UStUqDV994i8bYmDNPwLVHO2zEseOUVNQbTc7MTmCIsEmHtgqpXF4kPX6MrJSceKjOXVFod4P0828Ipz6CkhwWp04qC0kpSI63Wb7792Qnbcj89fbb87zz+hWmJ6p+PnmNGuNyg3a7y7MnSxhdSCsXb6QgjELOnjuDVN4Zdbg2uK+T9Q0OnzylnWRYa4iVohQKEgmdZoNg4Ty7wUX0uRLBo39P/+gpK39Y48XXf8/f/+cf8rN//uf8ybtvs7BgUN0dnn5zl0/vPGN1N6HXS7BmQNqVWHn6ACpRTEVBq5sgrCSMSsxMTVEulanWxpAqHuYdQjhmXBGNG2NBRYxNzTHRNxgb0N3fp714g/L5WWRnAxWXsIMO2jpQSLX34IsvYHESMVEZroVurxrFmpIB3d0X7Gx3GGnWuLUtUDEvns1x4/I8E7EcxihCWG8aYzk+OuLZi1XSrGADjwJhFYScWZinUSsN908Y5TAiSejefcDh+h7t1DFsoiBAypBjIaktLtKu1zjeFWSlBQKxS3fvCc9/vcbzz/+G37/9U3728z/l/VuXmL90mcneBs/vf8EfvnrK6s6A/YPjUxxFv8oISyChFEWEMVy5eJ7pmUWEir4LrriADYVj62spGVjI+4aDgz3mJ6YwYQmhBSiJNgKZ5Yhco7f2sUtP3Ro6FmOFi3Hx4bB3iwEh2Nw+YvnpEoPuodO3M3q4Z4YCzp6d4dL1a1QbU2DVKN8u9lUh6LR77O8dkCU9hvIL+OuXglqlzNzcrGPeForIPiYSFmyu6W3ss/t8BdIuKc5wqRmETKsQVQoIzy+wH46xfNjhwZMH3H/whLffuc+tt95j7uw1Jq5PEtVukKkS3b1NynWLDAzlibk/OiL/cE0bXELj7s3i5dgZao/4BLqgChfBYLvXZmpxgezRPcphGxuBzhUgsVmOyDLE5jrB1Czat0I569lwVI23RZ7mNqxGo87kWJP+zgCsC0wdagvdVodHT5aYmZx2aCceDS0mmH+ZBdA67vHk0XNnwY0dio4BhJFiZmbKBXCMAoIh6FNcE54Sri1s7xL2W4gITJZgtRNCzrVAlyv0cKwHxw5yC7e2BeZTvPxFunj6xxCgsCDCEJO7M7krEcP7woNRwloGeUo8PYXY3cHGFXS7jRQCVaqgwpgg6WGPj6DidISEcZ+UJglJt+0SNh8wGp+cV0plKlHIgc6/c3ZrBP1el+W1l0yPjxEGrhIv7AlAwgN+xgMSFsvS8grafveJFa5ezXqdWrniQQxJKS4Px0CeGAf8Z9ksw2xuEFjtKia+9GkBLQS6VCIrVbBp5gC70ZVhhdM/KtwE/nGO757zZPNO0fRw8melb2MQQ4U8V3E1maabDKhdPs/RV3eRoUIpiRQKkWeQafTWFnJ2HsYn3LnEqFvUAuKEDk1cKbO4MM/zlTUGheuCcFRXkxtePH1OY2KSV69dpewVUwswomiP29054MGDRyRJMlykKc4rBOfOLRKFLomyQ5MGN6eV4Dt9qUGmyddWUVmGUL4tyIuqpakmHyuRRsoHfp4JJkZzzAE2vk/21EGbk1Ch9bmXPNEyNVqrkMLdayAIa2VkGOMIUQNMrYypVzG9Dko7+rXINebgGPb2UeMToELHjLSjJHFoN24NedKjfbBDu9sezh8pYdCtEoaGwwtnaFRrQ7C6AOkKYPu43Wdrd49Wu0OeZ8MqsJCCcrlEvV6jVC4jRAFl+hgJz6CzFvoJemWdSucYaTzwI1w1PyuX6cQxCQKb5U4LSCiEdXpAea4xOkMECqkUmdbEcQxW0ut0GQx6ICzVRo2yB+BP69Da0OokXJo7T6+9TJJl9JMULaDT7TM+XudH3/uAEMgHfZY3NvnVr35Jv9dnv9NhbHoGJTImKuNYbUlKJUpTU5jusaf7W7AatbKC6g0wzUKM2M+RIur1U+XrP9zhl7/+ewdqF7pTQhKUYn76z37BB2++6dZFTqx5XtTDgel99nZ30cagi/HB9elPTE46y10/S7C5r0IJQp/IC22QDx4THR2CNUPtgALYSaZmOKwIpq41qYky/XbK1tIBg54hSRJKtRJT42Vam1vIsSaVEBrTFaISDPKEuckaad6idYrvo6Pjh8OWRGOLNj5JGIboPCNUgRe4VQgrkVaQK8jm57G7626tP8EqDTa3CNZWyG+OjyRIhjBwgaGK4bp257Nv+cu//CW5zVz8M9QIcu/BrVev8X/43//vkJ7NUWQgQwcqW4wVJEnG0vMXropYxGRidObpqQnq1fpQd8Pi23V8O48s3nJrkY9eUN1cQUr3IS7s0xgp6M1Psx2U2NrfQJOiUDQnqlSrJcgNpbjK9NgMYVSiUo1pVseZGp9nfGzKMTVNAUCezhGFIeVaiSTTVKsVMuvHxFrGGjXqE1XGJ6cp1+aQIkaiHIMWvFufu+c4DDkzPUtJJGANrYN98kvXyc6fpbz0HGEcyUYLQWgMdmMNcfdbxEc/wijHyi60OdyKaZlqRMSyz2Bw5LUy/LsnYf8g5d79R3z4/QnCKPCvt3tH+5nl/os9tlfXnMXtMMu1SAGLM2PMnlnEGs+m9ULXAsgOenS+vkdyuIkwztlkLAwIwgDGI3oXLvP5Fxvs7T2ivd8lwNKoCPZ2HvG7//czHn79ez748c/5yY+/z7VzC9y8WCFofcLHX/yOzaUNrDXI4HRjHIslUwGVRkjvqIO0AhVGnJkZpx5VKMVl9w54zTkhlGONe5t3ozWVWoOpqSna3QybdNnZ3OPizWvoZyuErRSTajIVImROaAbYB19jr12Am+d9/iJGLagu6WB6dpxKZDnuJox4MO57x4e7/OHzh3z0ozeox16SQQisMAySnK/uP2Zze+dEXmR9OmGZnZ7g3Nkz7p3C78l+nzW9Hgd3vqKzt421FmUEkVLUwhiUIrx+mY3jQx789mN6nQ5pv08cSMpjIdu7T1n53Qv+zf073Pn+n/HRn33EB7fPcOndChONnN/9xV/yybMntExApRZD9XTHUSrJ8WBAYK13lBXIIKQ2NkalFLoWcF9cdfGfHILORRSLkFQbTcYaHZIkx5qETjenevkKYukl8rBNbnPyqIQxOWGeIzeWEI9WkW9fw8TC70Hus6wfy2pjjMnxGus7rRHT0DNltE14+fwpD5cu88a1eSq+O6ZYufu9PvfuPWJjYw/tXYbdMurc6eq1JleuXCKQTqPUFU7c/QtjSF68oPfNPdK+W5sjJWmGrn2csAzXL7Oxu8Lj+/fo9gakqevsiMshe7tPuf9XK6w9vMOdD3/BRz/6gNcvz3Ph9o9oNCI+//Vf8f/Mu6c2hkOmrYByOaIex1RDy7Ur52g0Z5AyHO5hxliUEF4fxgHljbE6k5OThCaifdhh7vw8Zm6apHNIaLrY3LPfkdDNyJ8uE5w9h6iMYaPmibXuZCys2D9O+frbFQ62lzF2gLW+20a4NtPj43nGJmtUanWsLPkcrcAQLGmi2ds7YGd7hzTrF5nbcFsNg4DJyXEmp6ZgKDhuGaJwxpIfttn76jGdg120TshtjgoC+oEgkYrqRINwYY56LeTt92/xyWcZnzz6hqWXf8e9+8949dZNbr75BjcvXOfcf3ed4/YN8jwlS/sM2p0/Oib/INDGAsa6DUEyQoQdqOIRKEb8kOGNAr3uEcHiJbrVOjppEycZVZG4xTdJETpHHuwiDnYQMwuOLlcMkg9MhK+QFw+vVi+zeGaO7b09MlO4BfmJpg0P7t5nYnqa29euEhT6EEUlys+AQWr4+u5jtrbWHQDlU+2CpVFvlJmbnWKoRu1m8nACFNcirWsTEP0Esb1BoDOkUUiTYXNDnhvSJGfQjEito3DldohjInGtStZRcpBB0WN3yomiHSWfAkuoQjKfNIPgpGsWQGGRngz6mLEmMsvRqSaTylEHdYbVOUoG5Lt7yOk5dCAp7ImVlNQajWFiNnIeg6hWZmZinI3dfQe2eDCnqGY+e/KcK5cuMDXWGAIzDvG0w03N/ZqgddTmxfL6EElFnACgEMzNzbhWMBgmEYUSg7KCoq5S5ELm6BAOvPOJMZCmWO2q0FmaI87OMDB2KFw7DGZHjxkYiXOd+mEtg6TPIM2oVStO8JkCiDxZI/LPTRaAqwMGva42oDnc32PhymXyZ0vkgUKkmhLasVSyHHt8AOuryHoDE/rExp68FDkKRoTg2vWr3P/mHhuttnOm8m0W1lo6hy2+/PhTjo9avPnKdZqVirfotJgUdnf2+O1vPmZ3d9cxmxhpK1kszbE658+f8WPpQZjhYZDGM1Q8mJetrRK2Dt3zMLix1GAyTS5AN+pkJ0YMO+JzmQLLw9v2/iM4ublc2a0AUkiUVC558ONY9HZbf26DBwVFgEU7lksUEoyPYQ6PkFojdY7QhqDbhxcrhGfOQFjCKDkMUGRBR7aWcimmVi4ThwFt8uHc0Qb6gy67u3u8WFljcnqKelwG69a7Iuns9HIePl1lZfUlrfYx2ow+I1CS8ckm41PjVKpVhHAU5SJ/9LUnRJpjtg8QyyuotMNQkFpIdKjoNascCkHa62KThLhUplqpomRAIBRxuYRUrrUhMSlCKpI0Jel3semAPBlgJJRqZar1iVMdQykkNy7cINKG7qBLJnImx2rsDxqkeY5GUqlVqNuAF8ctLl6+Rvf4mJWXy8hSiYmxCS6cWSQOFLlNSMnR585i19YQJgXrRGfl3i5ydRVx+zVGrNMizBjNTRUEHLdbJLoAvjx0LAVPXyzxzuu3iYXf+n0wOtxWBRzv77Oxu+MZAnZ4iiCOmZiaHFLFrd//hFTDcwhAttsEd++idDpchwuHSS0DugvzrO5u8WL3JapUpnPY5nCvR6+b0aiEVKuK1s4OqbHMLk5yuLFDazejNYCDvR7Hx30I5HccI0/jCEsVEAKtMwIc2CVxLniaHG2dEGrxtK0w5OQMblyi/vgBQdLCKul0Zqwh6PUJv/6a7Mp18qjs19/hojJ89sV606iV6fWOSHV/xOQVRUxlWV1+zs7uNvMzC0Nw4X+VZlkHQK2vbbP04sXQjbI4d7G/Xb58njB0LBfrQW9wLFepCuAY6PQp3/mSMGvh3sfRqbSUtM6e5WX7GFFxQs3jU02CsqGftMkslENJUAqJShHXLt8m6SUYLZAywmpLr396CQY4bcLm2CSt9jGtZAChQqY5Aku5VGJ6csJp+QjXfuHyAYWVXoAeS4AhkDA5Po5NjsjzBCssrdY+42+/QbK2QtDpYKMAWR1D9geusPXNPSqL51DXLpNbS26cE5X0IMr0ZJOF+RnaB/sYzIiRbCBNUu589iVhaYKbr16nUonAWnqp5fHqEV/d+ZK0tcFId8ftwWOVkNuvXyZqTo50knwBygw0+589ZOPFffrG2b7nQAlBWUhKZxfYi6aItpaZbm2QD1ogBOPNcVIj6bQOaa3e4Zf/02O+/MPf884P/oQf/PA9br/zT7h04wrLdz/m0b377PfcmnNaRxAEpHnGIB3F80IqKvUJylE01B0ExyQcGsJax9yVVhDHZSYnJgiCDiY1cNwinz4Llxboff4IwgBbqyPyDDEYgB3Q/uQLavUGcnECbS1ZmqPUCBSdnp5lYX6G1vO1YTeAT0+w2YCHX32GUYJbt64yXi8hhaXd7fHk4TO++fxz8mwwik/9HVTimNdeu874xDjgbad9bkWW07lzn8Onz+jo3DH1/YIRW0E8NkZ47gy761vMiAxRgtUUcqmo15r0egNMnpF07/Ho71ZZuvt3fPzmj/nRT97nnVd/ws//j2/wveVPePD5F7zc67Oy1Tq1MQSQUjHZqLlYDi9NIAQqCAjD0MXZRW431Hwp1iInbitlQKncoNEckGcpWltsr00+tYA6O0vvYBVbqmArETLpI5Oc3CaYrz6nMt4guLlALrQTgMYgAyeFUKo0uXjhLE+WNumljHIPLBZN53iTz37zWwbJ93jl0jz1UoDA0Do+5uH9+3xx5w79JBnGi66VFFQYcvnGdS5eODNi+hd5hQWzu83ubz5m++CIxIPpuXVuWWUE0dwc+VST1rNHzMqQTqwZDAaoIKRRH2fQTchNymDtcz77n5/y6NO/5bUP/wk/+uA9Xr/0E376P9xm+v/2t6c2hkK47pkAQb0cMt0cIxKCOIiciUgRv0mJLNqXpM97jGaiUWV6YgKRKZK+YWAywrMzJBvbyKMONsuwQQBKOkOinTb26XOCqTnyqTJGRBTah8OHaC3jkxNEtTHyneBEvd39kxjY2jtgfWWZiYlJKo1ZbBHv4JjZRweHrC6tcrC7hdGJjzeL/VFQr9U4e3aRcq3pYm8x6l2QWEx/QPvxMkcPHtFPW1g0sZTUAkUkFb1SSO3aOQ6M4nBpiVKpToUEhOKwZ/j9ty/59tE6N7/+lne/9zq3332fy2dvoqNJDo4bLLf/+Jj8NzBtRpoiRR1o2Odl8S1IgqK1pqju5IOUTjYgOn+O/vPHZEIRZxkhGtdXrWGQY1+uIMcnMXF1WPUaJr1DGpY7fxiFnDu3yKOnzzjqdLFYcq0JcJWOztExf/jNH4CAi+cXqZcir6Xh1sQk0zx8uMo333xNrhOkdDpgBp+UCJieHmNycoKhm8PJoGv4TEbXJg4OUJ0DbxNdMC8sJs9I85xBXCL1rgGCkfBqwSyyBnJrnAgbhbDuKR8ODUIAkZSkw4C9QCLxYyeGvfVZkpJNN8nDEtneHqJeccm49r25eY7d3SE4PCKfmkRbT8kPItSooX6YGFgcEnvp/FnuP32ONqMXRvqKyf7uHl/ff8wP33+HilKA01kZJbSORp7nhvsPnnB4dDRKUqynSgNhGLC4OHdi/AR6CAUMzQeGD0dlGru2itTOTcxgUUiwGpPm5FqTNevkOhsF8P55OeaZWwCKFrPvEm1PaQitITk6Ym93m9Lly8hS2W18Qo/QmeILY1AmRxRVOuvIQy4pENgkpVPNaczP0X6xgogCtFcUs+DayVZXCWfmsLPzHmQwo3k/bLdyz7LUbPDG26/T+v1ntAbOycuYkap77/iYe5/c4cX9R0zNzjA2MYZCcXx4zMbaGoO+e5eLDKPQyiiVYq7fuMrU5OQwRy3Abzdyglz4ooa12L19zNJTZCEiisVqhbGWZJCRN5qYWgNtHChTCLUKnJdVIerq2qm+y106rUNIgVKSMFAEygn1jt4DxxwMlGNJ6QLEEIEDbXzQY5QgnWwidqqIPEVY7UBkk2PWNpHL60TXSuhGA62CIUHDuGZ9SuWYsfE64806h8d7QyDMAnmacXxwxL2v7tNJ4crN60w1G4RC0u8mbG3u8+zBC7bWluj3DtA6wdm1uPkXlyIWFs4wOT5J2SeuRXCEX/OUtqijLvrpCtH+NtK6FgADaCVJqmWO6zV6QjFRbxJEJaQKEELhzVkAibVu3ZBIyuUS/X6Hi5cvsL+7x8uX6/T6PYQ2pw6EW2uIJcyNz2DSjKWVVZJ+Sq3coD/oUQvLHBzuMbt4iZvXrjHWaLK9sUGsAqpTM5xpVnn49CFjM9MoaeiZY5pnF8jjiGjgXQaBMM9Jv/yS4JVX0JFnfhYBboHKAs1mk1C6xMe6C3Tf0Jalx09o9XtMVet+JE7MRQHWaJ7ef8h+q3ViHXPfr49PMjs25gE3H8IIRyVWyrGejDFw50tq25vDsS4aArCWtFxht17lxdJzWr2UIBH0OimNiTrVZk5Qztlf22KQW0qNmKSX0WnnHO4nJJ2UTjdFCUkUOUbV6Y2h9Q6DFtBYKbF5Dl7rIAxCpOuwI808I1dKRJ6TzE+RTM4QbvbAZjj2pgJjiB8+Rm+sYi7dpIBfsI7RUhQoCsBrdm6GKCyRZN3R2jfcOwQHB0d8eucO/+wXs4QqHIJkhYNgwdJrtXr86lcfc3i04/YDvxcU7Nl6o8rNV64jxX89/CvYeNKCfLBEbfmxu68hE9HFfO2xOlszszx5/Ih2Zw+hLEEpYLxe4/DggF6vS62qiYKQNBmgVIgQOZlJSZI+CoHVp9vilmc5e3sHTM1M0uv2ONo/pOzdf5RQRFGZUBWKsT5UHSZXRTwjkFJRq9VIxiZdUG8seaeDXbxAfu0S5sFDZLnCwEAeGMpWY4+PSP/276jVqtjFead7YixI95yjMOSdd99i9cUy7V5nmMwVF9PutPntr/+WR0+XGVu4hA0UnaMWu8sv6O6/AD04MRsstUjw3mtnuHTzNlaW0bkmLIKrVJN+/ZLy3c8ZT48ZODoclUBRjSOqYzHp+XOsb7UpDTqUw5j9MCLJE5QSVKpV6vWYVqtFknTorH7C363d5/Pf3eD9H/6cD3/0Nje//6+49NY260+/5f/6r//nUxxFweTUBLs7Hjj2RTYRRsgg/E5hDvC+Hb4F07pEGKmoVcYQWqFLGhUI+v2U8vUbJC9eIn0ckOcaEUQEWQ5rL7C/Hyf40w9hvOxY8oW7EBCVqrz66k1ebmxz1MtPJOSueDToHfLg09+x8vQ59YlJrIDu4T6dvU2ywQlAxCMvUaC4fvUSr9x6lTCISRNXPhJSgNb07z8l+exTptIe2sIBglhKxqOQahBQunqezRz29/aZNYJQxigpyXHzt1SpEpcieq1jeoMe4vAeD/7mGUvf/D2/e+8n/PhPfsD3Xv3nfHj1Bxwvf8M3f/gt/+dTHEVrLHFcYXKsSbvVc4x2AUIFzpXHJ2JD0IOiLiaGcxwMQSgplWNXDAYkmsxEhK+9SvJyF5mXIMnoDDSBUETpALGzjPjkc+T4GMF8DUPif7dYLwMuXLvBpUfPeLCy7/Sfrd9phcCajMPNp3zyyz0eT81TrVfA5LQP9jje3yZJ+kX6ALhcI1AB58+f5/3336ZcKpHnTiNNKRdf6qNDkt/+jurLDerW0sEVBpV05jlxtUrp2kXWjg443D9iSkrKShEKHycGAZV6k7gUcny0T3/Qpbf2OZ/+vx7z6NO/440f/JwffPAOImqe2hgaHxcL4RhezUrJFRZPWGpbnwKYAjC2vlvFGGqlgPHxOllPI0xGt9dnfG6SdGoajg4QoYY4QBpLOJBIbUierFGaekxQbZJXJ8mNbxctmFnCMDVZZ2p2mq31Ovmgj4+OHeRmLQfHPR48XKIcl7l41VBpjCNUSJ5pDg5bPH60zLOnT+l1jxBWnyD0C+IoZm5uhnMXzxPFFYAThiRg05zeyhYHX90n6x6gbYoUlkAqqoEkCCVisoq6coGljTY7L9aQaUI7SQmyPuMzTQYyYm1jk52vlnj44iXvfnOPD37wAdfffJfp2SuIq1N/dEz+gaDNKD1y+79PCgsl5QKkOLEZjSpLlt2dLS6cXSBbekap1UFW48JpHWEchZWtTeTUJvL8ZWyRDfqEadhWhgdWpOL8hfNcXFzk26dPyY1xKuACrJQYC/s7u/zqr/8zs+fOMn/2LFPTU8RBzKDT4+XKOkuPHtDrHGCtRkqBtJLcL/zlUsiNa5do1McKgI9CDMm94ML3FvuFp5cgVpYItadrGbBGYYwmGSRk1RqdMCLJjW8RGj1LK6yzQMN6hyKvfn3Kyf7wnK6EgQpCtGEo2gUF3uiS9eFAakN/kFE6f5b08y+xgUALgQgtaI3JUkSWo5eeoJpvYZRyfaSCEyi6e4gWO6Rfzy2eYWq8wcbB0QgL879n8pzH3z4giku89eoNalHgfs+3shghyTPD46erfHv/kaci/q8FnKdnJpieHPeXIPysckm5sCN2UYHLiYN9gsMdB+xYnKaPAqs1WZpimmP0g5KnRrpgT1q/aLkomiHiUbwNpzyMAmiMj1Ft1p3mk9EIVQTuDjBz4IVDicdKMUlWRQXiRNBjh9olAkH17CLp2hoyHWC8KDT4irruki09I6zV0bX6CWDIjQWm6Nx3YmQXr15hZ3uXe0+W6OcnBEOLjdFoOsdHdFvHrAgxnHMMhTFtMVxunkrJwsIc169ecS5RwzlVVKb8Hws3rX4fs7JM1O+Azd2ibi3YAK0tfWMRM9OkcTyadycOO3Q2KlDMgkt4uocUIw+tAgAUUvq/GAWsKnCBXPeox/H+EsI7OlXiiFCArVcwE+OYQYrNjKPRY6DTJnv6FNmoOpX+egOE8kGRQAhFqRQzNTnOhbML7O/vcNAdOaZZLDrP6Bwc8OSzL1j55ilB6NqOtNHorE+adNH5AKszD8b49UNKpqfGOLdwhnq1jlQBeJfBYh1QAO0ebO6gNtZQedfNAcBIQVYJ6U/U6dZrRLU6QVRGKTf+1li01mgNSO0AeV9tjlRIJ9OouEoUlKiWKkgMJRmgxOm2ZEgp2Ts+YKrWIFIRP3jrfXY2N+gMBmzv77G6usru3h6lcoMzzQmSLGN7Z5OjQcLZ2lnu3/2GteMDdD0iKgvopPSn5qjUm4R9T5P1IHT07Dn59hb23HkKir7wFrTOyUQzNTdLKY5pZ/0h+8/6dWnn5QafffkFP/vwRwRFm437cITWbDx/zse//5S+yd16NjwE569eYbxcHbUN+O8M33wBcnOL8p07qHzgLaXd5wvhKsDtC+dZpYOu5dTCmLSnMbnBYogjSdJLIFAE0mASQ/+g70RJpcUqgc2hm6b0uqcrROxYm44x6rDtE1o8RiODECFCjN+7siwlEJDnOft5l/L1y5S3XyLz3McqTtMi7HQJfvM7osVL5KWKX1csUrm5KqUkzxy4Nj8/zczMDJ3lg+9U5MEBg2ma85tff8z8mbO88+abhDIY/pyUEm0tSyub/OV/+jvuffM52rhKcKHHUAzahYtnOXf24rC18eQzKMBWIQTiqEf1s6+IEsfAGOp6WUuuJIeXz7GaGfZ7+wxyQ55Dr5sx2aySdI6xiWR+ZoHx5jQvt1cw0oKU5HlGmnYpl2t+LT+9w7GsM7JBwnhzEp1mJIOe0zVUzpCgaMMogLeiviGss2d2LGFBECjichlrQpR1jKukNyB843W6+weonUN66QCs4ZxQ1KxBbm1h/vZ3BH/+M8TkmJtHzqoULJw/f5Z33r3NH/7wKYPMFVFG9SJDv3fM6tOvWXt+z8UmRmOtW8uLdlSBpV6SvH99hre+/x6l+hzGBhibYrSBHPpP9jn47A9E3S2mo5CyEBglqVQi4lghrs6xFjbZ21njjExdYSBQaEDnBiUVxlgmJ2bZ2dykWYqJpeZw/Ut++T8+5PPP3uCHf/rP+ekP3mThjZ8zPvnHk4x/6GGMBi2ZGJ/kYHfXFc2Ee/5SSdcqLARCSbfmG78zewViF9oqwlKZODNYrV3i3E9gaprwrVt0P7tP1urTMgYtYc5CYHvIx3fRpTLBz76HrEY+lHOlHKXg4tUbvLO9zh8+f0QvxRU2fdYjyMmSY462WhxtO1arNdqv0g7ckd7gJFKKWzcu8tFPf0ytMeGkBgKNxWAzQ//5Kjsf/4Gkd8BYGDAvJWPWEFQqRCognJ9iMH+GJ4/X0VkOQYgUEKmQRBu0dmOYpJpSfZw03ycQiokG9Fp3uf8fH/L447/jVx/+gp//+Y/53pWf8uMLb8D/6f9yauMosQw6fTAKaYTfZiRYifL/SJ/gF9GVi8s9Ix5fUJWK0Mf+WJf3mUGGnr5E+PoG/U8fYzopx1oTScusAmFT1MpDxK9qyJ//gGiqNFq/JBgtaE6c4Qc/eJf9479l6ygdGncZ/2PWZAw6u2x197zQMENAwniAQPh2oDAIWFxc4Cd/8iPmp6cduIDXydIG+j1aX9xh/+E9GkYwHUZEIicLAmqlEipQhOfO0h6vs/LwkXNlKoVEaAIhnR6lNoRKkaWaSr1Jmh8SSUk57NNZ+4Rf/dvHfPn5u+wdnS57sXhsSJBRQJFBGcywlQ9sQQP3v2ARShAryVi9TBLm2MyQJZpsTJNfmCXb3yfOBFoPIM+pKIXSYPb6DD67S31sHHE1Qgc1jHFzx3pQuhJLLlw6w8v1WXbX21iTI6wuZhCphuWXB/S7X7G9tc/C2UVKlTqdbsrzFxs8W1rl+PgYY50mkfBFDxWETE9Ncu3GZabn5rBCoa1zhxNYyDL01iGdz++Rrq/R1wNyLAHu9yMVEMUx8bWz7KHY2zuiFggqQYy1lrYUVCJBqVrj+LiKkrDTTfjLPzzjm2+f86N37/C9P/knnL35+h8dj38gaFOgTQUO6rebQgj9RMLq1zoKGTZrBVk/odcICCankRvL2FC6ZNiMlNSDXJMtP0fNzJFXav7V8Oe01otYGvC6IbVGjddfu8nqy3UOe70hzdtRJcHanG7rmBcPOqw9XyIulwlUhMkysl4Hkw8wHqeTHlH0jtssnBnnypWLKBkOg88Td+fv3gM2ucW+3CRoj1pqrFGO/pZa+ommN9egayWmaEEYJr5u0Q+86rZj10gnaP2dwPk0DxfNy0A5EdZiwIYvXXHuYSrOoNumdv4s4uFjekKQG0M1TRzYpr2L194mdvMlLJ4FHGtGwPAF97cKODpyqVHj5rUr7N35iiTXw4qn9PTspN/j608/Y3tzm4uXLzIzNU69XAZr6PX6rCyt8+D+g5EWh3BgRRGMhmHAjetXqFaqLkAaCiwLf10UzEgH6CQpZnmZMOk7nRMDxghyKcgyTT/TmOlJV7miQO6FB0D87BCAbw876aJ1uofASolQBbAnCJRy7Cako/cbSxgohAgoL17CJPNoORpRIV113Nlmgm4ckb5Yof/iuaNvCzGkHmMsdnsD/byGvPkaJgpPbLAu6cMW9Ego1+u88dab6CznwdIyyRDUccdJBxRRtFaI4q0aodoOsxFMT0/wzrtvMDU5iSgS/5MgiovaEAikzrGrq4jNNUSeDtsVtAWd5Y5lU68hpyecKxkFOIQfQ/8aeGC20OkSJzOc0zr8fOXEXQ8bMT0AIYXTsxFCUUEQGVf9UBKUd1gykSCfqKNaLWTSRww8C0xr7O4udnkFEYbIIMSUq+6zpXdXCRXVRp0z586ws7vNYHmZfpoOW1qstVidk/Q6pP3BqMpi8SunRmAo2HsCBzBUazFXb1xkYmqKKC65gM3R43zCaKCbYDb30avrhK1DJHrIzMijgEG1QqfRoCMUIgw9kOrfLa/BYbFurZWGAsyTSvpE3BJXytTqDXd9wyD79I7caJbWXrCyvEJH99g+2qXWqBPXq9RqNZ6vPCfVKZtbK5SkQWddnm2uUJkb49nqM/K4xLUbN4nKcGAOEXmPtpymsnCG8t4WqnAhwVLqdel/9TX5mTOIsHARE8P7ytKE+niTqYkJ9jrHnilph85CeZbxq7/8GxqlMm/dukWpVHJOMK0ez+7d56//6m95sb8zrMcUS2MQlbj9xm1vw1tM0BOimlIgegPUHz6lerDnf1e4CCN3EEhSb7BzYYGt3gtEHBJKiMuCIFTkueXosE1lrEItFqSJQeeSsBoxXZqldXSMFANMBsm+RkVef+S0DimRMnbATRiSZTlKuKRQGHeXmTEEQYi0LpCW1pLllkfPXjBx7TKVrydoHG4BPhk3yok3P3yG+PoLeu99H6OCof1r0aIopCRPEyrVMq+9+gprL1+Q5ckQ3HSHm+sH+4f8P/71v+HBkyXefP11mrUaubbs7R/x5NEy33x9l+ODl1jrqvaIUZHCAHEY8uabr1OtNNw++F95hNKvfeGD51TXlyh4qaOQRZDWqrQWL/Bi7SXtXoter0/at4Rhzvb6Dp3DLqVGifHJWcKgTJ65wlWuoVGboFxuOG0xk53eGLrbZTyOKGtN1m4TBRGZdbbz2rg1FCXRCqQ1YCRSufY9MaThu7uUShKFERjlol0hsDpB1McIb77C0dZvGBhN1zix6AkpqGMJlp7ALxXhn/4JenLMxwcAgjiM+fD7P0DqlC+/uUu7M1pnsUVlWyN0NioUUuRMglAJppoBb7w6x+vvfEB14irgWpajOMYMcgbPd9n9+FMO2+uYQNG0likFsVJYFHKqyuHZKzxb3ifvHKCVBKvcu+338yBwf5cMEianZmgdHDI1Ncn0dMju0QH7T37Hf1y6x+e//oAf/uxPSc3pibtHSqGSHulQ8Nf9N9UaI0OscA6ERpvhZlloM+HfJ8dMCFFRiDAKKZUbv9wSXbtBZ2mFQbtNJgQbBrrGMi4EzbRFcPcOOoqJPngVxsrDeElKRaU2xrsffkQkDF9++4KDbkZmYCS0XiyaRbvFqNQjBARKMVatcPuVK7zxvXeojk05IFdAEIXYQUb6fJPjX33K8eE+gzimayxT1tIQAdICpRheucbyziF72xs0ygEmL2aI8MwxSxAGRBYGg5R6vc7xcYexsMzZyXHarWMO9u7y6P/7nOUvfs/nP/wZP/zojyeK/y1HGAZMlAN2D9tuvKwlz3K2jzpOe8rHywYn2Dxczwu3O2M9QOeS6cLFR6oAgUUnEN18jcHqGr1WjwGCFWPYF4IJLOO2R/TkU1Ca6KMPsLN1HyPgmFtBzPyV1/nFzw2/+/hTljePSfKTUaVfD2yhAepGuGjIdxqqinKpxJWrl3j/e++zsHjWFeIERHGI1jm602Hw6RccfPktxwR0Q8m4tdRFgAojkArq44jLF3n+cp2DwyPqUmH6GfJkTIUlDCXSaJIsp9mo0en0qFTHmJ6K2D8+5ujxf6a1v3lqYyilRBlQfgcJVOieixj12wiBy5/x2LQQQ/YiUhJHEcJWQBn6gy5b25tMLsxgDs8weNzDttqkeR+hBHUgtiC32ujf3yEIFeH5a9iw5p393HgEAi6dnWLz6iX6vQ6dQ4PRPbC5Y93hNMde7nfY3H9IeOexK7YYfPZdIAvWew0ogiBkcnKcV25d49qN65TKDbLcseik0ohck+8d0/v6EeHyC2ZlilWWIyNQgaIWR5RLVaKZaeyZBe4+XKbXadFAUxGKslSOGBJIpISJZp1SOebwuEu/12czSfm3v3zAZ98856OP3vijY/Lf0B6lhxvJMBctXHMQ/0W7gjwB8jh7xaN+j6lzZ9E7L9HJwPWM4hFy41RGosM9stUlzJWb6PC7m4GQFsyImqWU5Py1S7yz8Qq///wrurnj7hhjh33axrgWi7SXkw26XpPDMTa+wwoyYljubjZLvP+9N6jVJ7zDTWH3VWS9hWCYR1kOjgheviAwjiplfMXGCkGSZgzCgG59nCS3w0Tfwohx48WcFb7/33rK2akn/B4hdggGUik/ec2wglo4igwfhp/cJssYKEV84Rz9p0sgBEa4fnFhfTKWpPBiCTk+gfGMDOsVt52tpv98/wwDqbh+/Qqrq6s839xF+3Gzw7NClgxYf/aUjeVlokqVUqmEFIJ00KfbaWOHivzDWUKxmCwsTHPt8iWnF1T8lCiewjD7cPdgLPblBsHBthtfX+ly802SDDR5tc6gPk5W9I3Z4dOEE3PJfagdscNOO+F3aAYOGPNgR+4Q30jnDvBDIFSMtQIVVNGJdIyEMMRahcDZ2hpj0FYjygHNG7fJt7dJ2h2MsEhjEcK3lPT75M+eEEZl5NVraFUI4/xX5qiSNGaneP+H3yMKFQ+eL9NJs++Kgf9XbuoEpofw7/fc3DQ//uj7zM/POxFQ+A6TqnBuk0Kico1eXUc8f0LQ77ggxrj3yFpLNkjpa0t4/hw9pRwAwNAbklFGegJUtaOW0NM+TiqTDBkqtpDH/u5zcla9ikicuBrrnpoRgrxeQTariF4PkSXOIQOL7PVgfQPiMiKMkfMhJnbtNa7TQRKXYyZnprhy5SLdbpft/QP6aUJeqPJbcOh8OrzO0Vi5rwodDCkllVLEubOzXLx0kXK1jlQhRVDp/4dMNeweYdc2YGMTOehShERaSZJKiV69ynGpRM9ATSkn+GeMY3ZZnE2nNuTKB+qyAI0crT3PNSoIiEolwjQh1ymjsOt0DusTvk7aRwvNytoylxYW0AgqKiKwcNTqcPfhYzZ3dqlHEZsHB5yZjIkmBNPRJNr2yaWh2+uRdHtUyruMX7lI/vARSmuwudNr0Dniyy9Qr98mv3hppGNBIR4tKVXK3Lr1Cs/WVslsPhRjtz7xP97b59/9m3/LF+fPMzM5iU5z9nZ2Wd/epj3wTgp+TSlm4OTCApcWzrg3RRSAvheOtCCNRT1+TO3efYTOhu2QaOH/o+heuMDS8SHPV1ZoLNYp5SlpbhDGkg8ylFKYNCUTkjSFiWaVdNBHBCWUhM5RnyQDoQQyDhxb5bTG0BjSNCUMXIu0FK5t0VqnhWAwaJ07kV4pHCwuLFkOW1u7PJufpPnObaq/PkDlfn46PjBykBD+8leEszNkF6/jVTeHcZSUTsvK6JwffPgu9+/d48Xakj93sR4VGl6W3vExv/3l3/Dx3/4GFZRcop/nWJtjyP2aVgAyxf7mYourV87x+uuvuzXvJOYtRgxMIQThXovm3W+J6KOlRFmLNQJrNVoqji6dZZWA5c0VcpNSLkeIQY7QFpMkLk7DsZS0zolUQNLpkeY59fosWZY5AfTsdG3bAyk5P+eEj49aHTqdtgffNYe9HmsHB5yfmsRYiUGgrHXdnP5ZFXGEQCKkIlARSD8OEqRSmCSldOUS4ct1+g8fY4FNo2lbyaSBepZSefIIlaaojz4iPzeHOQEWx7Ua3//oIxbPTPLo3n1WN3Zp91LS3JIPCwXFFu+KeZVQMFUvsXB2jCuvXGT27HXiyiyIaNghabo5yaNNDn7/KUdbj0lIsEKyZQx9KZkPAipVhX3lEpvtiNbLx5RVhpWxM9Hw5xMY5/RjFZ1um36ooBSz3e1ycWKRZhDSyyxB0mX727/h37/4ip3t07P8DoKAufEmG7t7/j1y+kAvtnaw87PUKs6Ovmgx9NVWD3IW3H2nzRQE3mlQONBF5DkmLtH44B2y7q9Jt/qk1rJnDYmAvjXUshbVL36LOD4i+Og9mG1gxMhGvVyf4d0f/xmL5x7y8P59Xqzvc9hJSHODZhTbC3xMIiAOJGPNKufPzXP9+g3mz19CRWUXu3jXOpukJPeWOPz4Uw521kl1jlGSrrUkUpBGEWNKEV29wFG1xvbTB5h0gClVyDDEAvB24W66CicCPOjTUyGEIbudHpWJSSYmyySDl4i0R7r9W3737+7yzce3T20MwRUDJybHsSahPzjCYMhNzs7uBplOics14opAWYEQRRvaqAugKFgppRzALV1eIpRACoNIB5hqk/JbbzFo/ZZwJ6Fn4MAa+kAfzVjep/zkS4IkQfzw+3B+AtdF7cWDgxLnbr7Nn42N8ejeNzx+ts7eUZ9BZsk9U7QIA4eHFIRKUauWmZ+b5fqN61y8cpV6o+FWEKPd+2407B2QfvYZ+1/fo9Xtkyvh55llTCqmrHXAxK0b7AjDxvo6WmvvvOQNtQsAxOeEykKvN8AGISjFQT+jPjHLfKlCZF8i7Sm2nPocRyKcs3GWEUQRVroc3FrHuJHypJSHGIbPArcmEwRoYWgfaZ49fMYrr6bMXpyhtbqLPGrTtSmps8yjaqGMJH+xg40+JzAWcf4qpjyGIfCxiWWsHvHaa5dItebZI0XncBOddn3BwvpWO8fhGgI1HhAsdkXnOB0Qx45tfuOVK7x2+ybjE1MIFKE0rhMlzcl2D+nce8rxoycEaZtGBAsyppHnqFJMqVQiaNYJr11gqWN58Wyd6bESBAqUQAUuj7S5wOYGhUSnOdVKjM5zIhFBpcp6P+Xf/sev/uiQ/MPVw4pWJfcHXCDh+2ssyKEuyigt9sMIwjLod8nGxpGTU5idDay2KBk4BoS0YA1yMEAuLRFUx2DhHEYWQYtwgdAQLXUbnIoi3v7gfXSScuf+Q7ppPlLnH17n8HIp3gALQ5TbulZmhLXUqzHf/97rXLh4HQg9wOGrEEXW79FEgSBIEuTyC6KjHTc9PGMEbbCDjG4nIT0zQxsXPLhrs27BtsovVG5BskBujGvvMieu/ZQOW4AR/tEoKb3AMxSY1eiRFdWCEcOo024xcekc0dMnhL0BxIEDLbT1zCCJPThALL1A3byFjYbUDox/wQubUmFdT2dlosn7771F61cfs93qnBwxij56YS06S+kdp/SOPcgiR8Kqoxlph+NTr1d4793XqdeqwwB66OJRFERgSDmWrRZ26Qly0PcLu3dJyTXWZvTTHC7MkajQ34NLboYcEuEAAm/E4no97ajb8jQPIf0CgMHmlkAEbnHTGcILSWorGCQ9106W5SRZj35nQK4N2khkGDinCmlJBwP6e4dUc6gsnsU8e0aWplhGDDSLhUGf9NF9wnIFsbCAVd6dxrPlCttuhGM71CYnee+H32dicpJvHzxi97hFZp1g4Enr6aHw5fD9glIp4srl87z19uvMzcwhC6WoIZYohj8vAKktemOT/OE94n4LYSxSSLSQWFMwpXLCcwvk482hj8Nw/nghyoKJZdFD+uso/zm9cRzec3E//mtrnZ5EUWkdPSsPVp0AxhECad1aksURptnAdgeo/gBl++4ZaI3dPwS1CjJARSWYnXK2vf4alAqo1utcvHqFLEkIny2xc3hEp9cnzTP3HgzZUSONseI6iiJZoJxd7sz0OLduXWNmZh6lSgghh89RIEAb5EELs/wSsb6OOt5H5s4tzEpJHocMmjXajRrHKiCTeIFmz64xXoPEGrI0xwiQXjRy2OYhJFmWOr0V4aqt6NMHUE1uaB21qVar2IHB2oCnq+sYIblx/hLVcszOkUXIgNmxWbpHBxgr2N89oDQu2Iw3qaoG6fEBvUFClqW0+wf0566TTE5Q2uiCLQJaQ7h/gP74Y+SZM5hSZTQjleufN1je/cH3+PzLr1jZ2cBVLj3A4tffbrfDt/fvj1J6b219ErYs5qYIS7zx3jtMlJ1Q75D96QFDCQT7+9TvfEEp75EridX5MDiyUpI0xzg4N8/dTz5lae+IV+emMNmAbpqBsSRpQvs4QShFtVkmUJKjwy79QYqKc5qNEmPNmHYnoFZrQJjTPzxdGrjVOVoGzu5bKW/Pa1wLsYU48vamBUvNtzdluearew+48MEHVB+cobG54h2JtO9yNIS7u0R/8dfwryZIJ2dP4CUumVRBgDE5M7NT/OIXP+N//J/+HUetfYqSwLAVzhYtWzlG52RZH3DrHCc+8+Q6ZT3bdXKyyb/8l3/OeHPqxODy3fUEUJ2E6D/9PfHmCwJhXXKgHQikjaAzUeflpcvcff6cg9YhSZKBTglE4Gn9AWEcoiSkaUIpriCEIE1TgkBitCbVfbCGND1dpk0YKKJyDRVGTJdi+jpnY7uP1had5jxZWcVqy/Vqk0AV7e14nSufFPoiolAKKd09WWG8g6VEGqdy13zvXczxEWZjhx6uMDAwEAWCssixz+5jWm3ED3+EeOUyhKMiR6lc59pr73H+4lX2t9fY2Vxlb3eX3nGXXj9Da1dNrtdCxiaqTM1MMTV3hsrkAmFlHCFKQ4FjDGStAe27yxzd+ZzWwTK5HqAEaHIwhhSFLgG3z3NcP8fBww100kaUY9+OKwmkICj2F+OA2JJU7LbbTE5PMOgPWN/ZY2psnDwxxKUS1ZoiTY5Ie8enOIqCSq3BnDQcdbsuzLKGQe+YZ8sJcVTmXKlKJF1Ce3K6C+ks3KVwYGgglXN888UJrEX3E9T4HFPvvYv69R1Mp08/d7+TG4uIJCI9hvufkLWPkB98D3F1DhP5tjojkHGdhRtvMXvxKm/ub7G3u8nRzh7tVpdBPyPPDEIJqrUqE2NNxqamGJ+dozo2jQorDhQ8kZ+Ygw6tLx5w/O3XtFt7JCZH4vZuKQTSQI5AXFokP3+O1uoOptvFSAegS+FY10oqRCHM6ff+qhL0ez3qk03yPGVre5/ZiSZpopFRRKMaYLI+g62PT3EM3bCEYZlmY5yNnWOMsUSRpKQER3vbPBARt69eZaIRu/dQusKvtcY5SgkvolAUl7V2OYr0z85qbD8hmLnI2FsHqN99g+kYejkoIcgt6FAh8g7iyR1kp4X+wYfIG2exoYueLRZkzMTiTd6fPc+tNzbZ3njB5tY2+3st2u0eg0FGnhtkqKjVqoxPjDM5PcX8wiLT02coV+sIHAtkmGJmGvNyg+7vf8/R0iP2Bym5damrtpYBcCigFARM3rhOcmaa/WePSdttRFxBCImT8RQEEjKfZxhtCbQhRHPcS6mPj5ENUnb3jpgZr2Pyk/PqdEaxiJc6/ZT1rU3m5iYo2xiL8dpt/2W52iKt8ePnckwjXcHtuJvwfHkXk7UZ/2iM8NZ5OsmAZD+hpwd0jaZpLeMSyrlAPd8htF+ikgx58Sq2NoGVIVgIpGBxpo585xq1Romlpw0Ot7cY9FvobIAx+VBKAzvqwhgWeGVAHJeo1evMz09x9eo5Ll8+T7PZ9LGqQQkQOiff3qN39ylH3z7guLuPVpqegTEDtSB0LfdRhDo/S7vZYPnxBjrtI2wIViKsQVk71NQVFuIoxOocawzlUkzWSxgrh0SNGp1MA3f/qyPyD3OPspAZ7VwbisQUT2Xz7S9DHkeRfPlBdP8vyNOMTpZRungR2zpEpH2MyZz6tHHoibUWeXyIfnwfWaujx8YoXEZO5ugFu0cA5VqN7330IaEK+PLBQw4GA/c9IdwT8i0FUgisMX6xd9dnPP1ZAo1qzAfvvsKbb7xNGFRcL/QQAPL3I33CJQQ2zWH9JeHOGoHJhuK4RfWjP+jTDySd8Qn6xuD0f04kDsUP4j9TgrVushVkjtM9isTUj53ybAvcJu2SRvffwuqw6N8EEANNt9mkfOUC2cPH2ASK1UoIp4ugdI5YXcJOTKIXFkDIke4Byvcl+nMIiRQhC+cv8ZMPUn718SfsdfseUPMvGziU/cQhigngx2RII/Z/Va3GfPjh21xYXBwt/oih+1mR77vFX0A/wz56TNA6pLCwdjiEAwt7nQGmXCabaKLRbsE/CfzgAUAz6okdQkhWYJCc5iELpzFP+Q1KEQQ5hBWS3KBkADonrDinjDwZEAYW080RgwylJFGjRlirIoVLuuTGFmJpj9rZCwx29wj3dpwwt/GgjfDNMK0W+tuvKFmNWDwLQeD1L0a8tYJdJYSi1Bjj1rtvc+7yBZ4/fMLyy032Wy16g8S35rn3S+J1O0oRk1NjXL9xjStXL1MuV1zCbU7My1FHpasOao1e32Rw9yui7qEDs4wFBUY7gfJ+lqHHxzCLC6RuovuxcQj8CaiQApCz1rq5d+J8p3kULWFDHalizSoSKn81w3XAd4aKE79VrI3tNIdymer4GGqQEuxlKFybFGmK3d7BJDkm16jSa+TNMU85lcNJXGk2ufX2G4xPjvPixTLrLzfZPThyY1W0w9iT1+8u0gnxScabVRYXZrl+/QpXbtxEqTJ4Rtdw7KzF7LWwXz1AbawTDjoom/q1X5AJQTJWpzM5QbvRIJMB5VIE2tnGY62jHWvHZU6zFAJLrpWvnDgwMwgV/f5gCOQIpBN6z0+3ui+EoFKu0qw2SboJ9XLMRH2K6dkFYgR76+u8cuMmKop589o1Np4/I4tyatMlQhGSDzQdOnSO2mAVOhfsHR+x2+hTu3aF2s4mUud+zXQM0eCrr+HmDbK333FuYn4+S6kwec741CQ/+ZOf8O/+w3+gm3j2jAfAR1uP+7zRWmZGc2H4cknmLl7m/bfedBWz7yT4PqDudAj+4pfEmy/dXA1CdJo5doaAPIw4vnGFuwcv2UwOCSJJ63hAXG1QihukSYvJyRLi5YBuP6XaqJD1ExoT45StpVGvofttekeCINCMTzRQIWw+3z7VMQzCGKszojBw64JOCLCMrGgFCIUKJDbXaIyT0RCStbVNvjq3ytjbr1L+m11Uv03hoWmxSCMoP36K+E//Cfu//e/Ja01/ZjtMSvI8IwgEb759m8PDI/6Xv/grOr2Wt3m2voQyeueMj7fcdnqC2eOtbUf7uKBSKfPP/tnPuHr1lnP7Gq7T32XIiUFG8Nk9Gg+/QaVH6KIwYXJQAQMVsnP9Eg+Oujx9/pysl5JnOWmWEcVlqrElyTKyPCeqhOwfbWKblsFgwOHhHotnLzqgxscKSdI/tTH0I4nOQIWuKFFpTGA298AISuWIUixY2niJCSNevXSZkhK+0OhiVqnEMDZ04yJ9zCB9IumJHcYianWmf/IR0V/9Df3dA4QxxEIQhiFI0HqA3VpB//VfELx8k+DtN7FTDYwqwFFFqT7FQn2S+UuvYfUA8j4mT9DagVlBVEYEFVAxSoXDeGS4dyWafLXF0ef32Vm5T9rfx5oEF3G5n4ulYqwWULk9z/7CKxytt9CtHYxNsUROtD7XRNq6eqjxhUwFUSmilIdIIZienmJrcxeb5mhfWKzWKszMjhPde3Sqo2hRhHEVgcTkmiCOqDUi0t6Ab58+RQdlLs9OMGxb87ozwmv6FXPBKu9kemI/DYHQCPLFS4y93Ubd+Zpe26KwlMOQMIoQ/QyTdcjWH2H+uk28fBP13i3ERNURr5EIGROVYyYXxpk4c8UlYDpD6wzrrZ5FECFVhFQhUgboYn/Xvm0qyek/3aD15Tfsry/Ry/vITBNg0cK59iigHIU0z8/DrVtsHrTJ9/fQ6QArjGO4+04GJQReLsttF4FCBgFR7DoFpsYn2NnbY3vXAREqUlQqZaqlJtbk/OGbUxxDi2tnKrRJPTtWqYB6TXK8v8rXwGvXrjJdLfkYDD+GwrO+hIv7fH4hvdGHkAFSeJMGQsTFNxlvdQk+f0JXu9bVOAyJKyVEv4tO++iNRyR/dUyw/AbRO68hZivYwK+lSIKgxvjsFZozF7icp5g8RWcJRqfkeYYIQsKoigpiVBi5mBRfqPXzUBiLaXfoPXhE+6uvOTzYoz/QoN28s9YxUmIB9Sikev0yyfUr7G1soPf2sNpprFjrtI2s1SgkqmAhCYEMQ8I4IhKaKIppVCrsHxyymQ0wqWuXP61DeK00aw2JMazsHmFtSqUUU44qCNTQbXDY7zAER0ApUIFCW0We5Rwedznu9Xn4fJ/pmQfcfv0ddP8iyd2EvL1LPxsgMAgNJhRUNajnG8h+D9nrIK7eJG9Og4oBRSQFZ6fqjFWvcn5hmpX1LTY3tjg6OCTpdsnT1Dkc+xZzKRVhGBLFEY1ajemZCc4sTLMwP8305Bhh4NyqEL442E/IX27T/eoBrcfP6bT2ycgw0rJvLR0UU3FEMwpRZyYxC/McHA1IWi2EyCnkPsAVV0+aUCtv4dtv91FxQColSZoyVokYnz8lIWJjcvK0jw1CRimaT9ils99zg2Y8KCBHadwwUNS020fUp2cRs/OEq8tD1oJDyP2maQxiawN77xvUm285fZsTAnLDj/QVIyEl5eYY7//kh0zNTvLNt/dY29n1or8CfaI3sKBTug/w26cSnJ0b4903b/HKa68TxHVsoZ1RCGPZAiJynyNzi1l5iXpyl2BwDFZT9ARbK9Cppt3tk128QEuVyXUOHsFz7J6i53UkchogsXmBDoI45WQfcOJoXswllAKJxmp3HU6/w45a36zAUOi3uNRn0O5SPnceu76JbrUwQqCkt0XGo+XdHubbrxFBiJ2ddS/BMHWSGGG8yJibLijJuRvX+Fms+OTO17zcOyTNzbCqKwowaehKdmL2M8ozhBSMN+t88L3XuXn9OkJFDjUVeCrqCBMu7kcOEsy9e8Rb6wijPVtEOvqayckGOf1UYy9foh9EPjgePs0hul4EBgUFr7gq8d1M51QO6W123ZyWIANEqJBxSJ6mGAKCKIQgptdugc6de0ZcRoURNiwRVBuoOMboFImgHJfJ9lOkyZg6f5PNfgfT6znGV8GyEDjQ82Cf/O7XBHkO585BFH4HXRi+JaIA5xTN6Wluj41x6eiYvf1dWkfHtLp9sjxz1XoVUm3UmJiZZGp6kmqt6e7NWtAOWLF2eAr/xC30B5jVNbL795HtI0f6C5wQudWWLDP005ysVEJdPEtajn2b3kng4wQzUDAMeAunrKK95B/l8AtkoRLmWpZ8AOpFZotzSxhe83dnv6DX6bPf7lJVEnHuDNKkqP1D125lLeQp8mAPfbePOW6j3nsLOTuDiaKhoKJAEJYqnLt2ncn5WS7t7rK9scnLtQ12tvfo9AZom3v8QKCEII4DJqeazC/McGZxgdm5M4xNTiNkfHImAGCSjN7qNubOXeqry4QiQQUu0LZCOBvh8SpHZ+bYUCHHnZSgKhkvlR1Yk+eOOSPdcqvznDzPiW2M0Rotcqx1m6EKFHmeI6UavqVaawb9wakOXxSGnF88x/kz8/zuaJ+N3S1qlQqRFMS5JlAQGc3seA2ddtje3yFNc44PuqSJpjpVoTRWIROHHB4cEDcnmZyfItEt+tcu0vr6Ls2071kbbm0OBz3MX/wVwdw8euHsaL32egZIyVvvvc3mxja//vh3DHTXt/n6woUHbMDtvMPW2BNguBWCuNrgw3/yY86Mj7ndUIrvrAUi04gvvqb84AFCD7DStVRaLFYJtFS0Z2ZYapZ4vPIYGjkTYUxcCRgbHyOqCvLc0ktTgiCiddQhOe4z0Jaa1MxNzWLSAQMkiIgogo21TeJafKooqmtxy4jKJd+6owmiGKESbJb69iFnW2uFc8wy2qCt25/zLOfevUdc/uGHlC5dZPzhfedEhHdvkiCtJvziS0wYYv/8n6Gb4/jyg3uXPMsnCAP+5Kc/pFmv8jd/8ytWN9ZJ82SodVUUC+wwFpIefHMxTVHLMNaRl6uVEj/96Yd88MEPvIh3UbHAY6keUO0mRL/+nOq9rzD1GPYs1jqeqMk11gra52fYWLzAt598yUH3mNTvK41GDFa5RDA0yECiAkmsythE0ut2qdRKZGnKYesA45kEcXy6QsS5MXQGPeqhJAhKBDLwbc4GkxtUFFKr11hee4HRhtcuX6ZWivwzGLX3S6xnMLpKsStKiqFeRagkVkIyPk75R99H/Pb3RIfH1ERAHJeh30NrTScA0zmmeef32KcvMLffgBuXEBNVbKR8+7REKoUIIogaFLvRyfew2JgkgLaYnkbvdLCP1mg9vsfO0QoD3UcYgzGazBoir4HWaISUb51l99JtVjb7NHa2nDOmBe313gQOsJLSiX1qLLkQXtdODteNmdlJtja3nD4SLnmsV+oE4elp2hhjyIxxMbH07Ght0D1LpVyj3+tw9+5X5Lde5+rCFPF3JAQKkLUA6Z1WivIFUeW1bZz+n8TevE05HSC/fEjZSCphTCgENs9JsHRMijhYQX62iXn6BN54i+CVC8jxCgTC5ywByMA3GwgUDq0YepYUKFIBkBuL7WfY9T3Sb57Qf/qQ/fSIjsmR2hWbEv97oRBUw4Cxi3OId99kpT1gb32dqTxzyZv2oI3v8A6VQuYZWJf35EqQK6fjJIUl14bJ8XG2t/ZI0ZRthFIxYalGZSw+tTF042hdS40viBkPJOdJhgwDas0qrcMV7j+yvHrjBjPeJt0VUp3kuzHGvR/uQbs1WKphS6kTj1VYVYXb71EeDJAP1oi1ohZXiQXkSUpmLR1hSNtbjH35K4K15+jbbyBuXECMlRBBoQ2nnJZZFEHk5hPg9C2LKMavndZXiKXAsfG7PezqBvnXdzlaWWIv7ZMbIDdIK5z6oBSEVlAqlZm8chnxynVWdndJN7ZcWy2QY9DCEwsMSFmkqxYr3TtphMQKQ24MMigz3qyzu7/vWIGnWOmXUpB5VnO5pLBRhbWDHkpucE2GNMZmGMIIdjTPi83F4kT3pZYkWc7+0SFJlpBazZdfPqHZqHL+0lXonafzMMPkB8Q2B2EQUeha4/o9WNlFd79CHHZRr9xAzMyj4zpWBARCMlaJaJyf4ez8OO3eRVrtPsetLq12l36vR5akSGsolyIajQr1ZpVmvUK9VqZSiohUIbCMW3ezHHPcYbC0TvveY7prq3T7bVJSB75oyLD0MLSUojI/ibh8nlYu6e8donRGUd8UUjo3SuW6FiTCFZSNJhQSnWuSQZ9SrUo/s7R6Pc7NjP/RMfkHgTYFpQhjyXLNIB1QLZcx2qDCcNiy4dAGO8JEcAuQ2xAFOsvY77Y4c+ky6dYmYS9zlQ7pXgJjLNpoV01YW8LGIeq1N9Gl8uhifIJYAAzSVwiDWpWrb77OzOIcLx494unT52zsHjEYwiNFZWmUCDVLAdevLvLmO28wPX+WIK4xgmf8y+LPVwjZKiuwL3eQj74l6B7gvGcdWGOtJc80vW5CUq/Qmpiil3kgxr/x0ox62oVw6KsL3CTW3fpQbvU0g1MBRAJUIBAWgjhkrlahGo2CluKFk8pVrQ0g2inl1Q6x9lX1sRJHl65y/OAeNsswWvt31RZ6tHB4SP7Vl8h33oGpSfe8fR7vEanh3znKecTC5av8dGyMh9/e59GzFY56g6Gq+2hvHlXCRvo4EIWK84uzvPPOGyyeO0uoAjceQ+ZCIRY8xBIQSUb+6DFydQlMBlpD4ESgMZY0MXQHGfbMNIPpSbLvLIi+4kWRctuRExWMhImxKHsS6DmFcRRQaFlIoVz7Q9HPbHDBjgzI2wPSlV1srglDRVQNUGWFVgqhAqQIsDZ1iVwYOKbCXkpUazB5+VVefvM5eZoMK/BCSS9UaZ3F6d2vEZ0O6splqFSGJR4xdD4rnr2z082tswRfbNRdcJllw0DYuQREqCIAFCNdl5Hb2onDWmh3yJ89g7VlZKflSjtGkLuCBcYa+mnCQErkuUXy5hjGSgcAnYBsXIwsfSZa9LjhUaqiene6Y1h8PLa4ktFmR/FVkUT4/6qCvj/8SQ8UWhcQdrsdjrHoRo0rlxZRWU6p3UUZz/UyGtXrop8vofsDxI1rqPNnYWIMEwSjOxSSSq1BXIqZmp3myo2r9NtdBv2eT+QcoKoCSRSXqFTLlCs1olKVMCwjZOCxKJ8IDDKyvRbJ+haD1RXU/hrYnn+mAVYojJKkkaS3uMhSP+dY5ATVEvVy5OjOuaGf9RwIYyHPNWmWYowmypw6fx5kBEGAkoGzP800UmTDxCQbZLRbp9tWE0YRrYM9NoRGa0szrjFZr9Br73HUGrB9eMxivcbj58+4k/RJBjkL87Pkpk9chaASkXdTjraP6PcHBOWMpHeEqCkOVU78+qtUf3tIlCfDtU4giLe2GPzH/wj/w79CNyaG24RSTji1XKvwi3/xZ4RRxK9+8xt6gw5gvsPyHMVZo/2umH8yKPHa93/A+6/dJBQjNhi+sq10jrh7l/iLr6FawR51GQa5HgjKKnX2bl3j/vYqiQ9eZDmiMR5SiQxZf0DSbyFkQGwslUaFRjNGZ5q0lXHEISQJO1v7tFp9ZhZmkMpy3Ot7AdfTOwyWPM9AgPTghtEWozNPz7cuR0KAClzyJwRCGLSxHB63+HLpGc03bxDt7VPbfekA7hOPNdQ54pM7SG1I/+k/JZ2YopD/EcFovVNhwLvff5fzF87y93/3Oz75/HOO28eMdG5OFgOG/Avfp+h08aSUTE+M8/Of/4gf/ugjyqX66GYFQ5YfAMc9wr/5Pc1PfkdQ0ujpWYxUCO2EcrUQ9MfqbN1+lU8er/Byd4tca5L+gKgckAIRjnae5RlhLAlkQLfVRYcSnaccHhyztX5Et33A3OIczXqDTKenO4bG0u71CFTgLMY9O9MYQyBAZQk2iKjXKjxffY7WmteuX6NRKrtao8/NrC2eqfUOf+pEnOJYN7EKCazBzp1DfB/kV19i13fIj46wOuVAGVpxiWouiYRmcLCM/GSX6NEZgvMX4fxZ7NwUohY63YNADgP9IbPch4xWW+wgRx0nyM0jxPIGrL1g/3CVzbxF34OpAdaLgUpKQjDeiKjdPsvB1ddY2RH0N18yphNXsCoYVoH0GhWuSu9CKov2rAYlJNYK0jRDqoDxqUl2d3ZBKIx2fDLxnfa8/3/H0JBkKUg71GsScUw1EGSDPqVSBZn2ePjoLrl5lesLM1S8K2ZhVGLxeig+ERfDaggIocAqwkCBDNC3XodyiP3qEelBiyxz8gqHUUw3jGlajbIpqrVK8Nkx4v4UZvEc4uw8cmYc0SxDKcQqNQTCCyV3V4822Fxj+hnsH2M29zDLa4jNVZL2AbsmoxMIMuHYPgKDVg78aUQxjcvnELdv8GS3zd7uHlGeDwHEwK/FVjlNGLTwchI419rUT2ohMJnGKo2RAY3xJgcHeyAFxhYF89MFbaw1DJIUk1lf+HNMzpnxOmmvRXsgaVaqtI83+fqh4Pa1a8yOlRG452CtRnrDmSK/s1YMBYuLkQ4QlJVAx03s2z/AVr4k+PYZ5mifNMsw1nAYxRwFIRVtyMWA/u4j+HgD9eAswblL2IuLyLlJRL2ELbJiMXoXvlN8KsZYW0S/j9w/xqysYZ8uYV+u0ep36ErXzma104PMhHufyiiqlTK161cRt2+xetTi5coaE8agpHTvGhIlFDJwLbour3ZnN1aTaj+/vZB9lueEUYlGo8r+YWt0fac2jqMvgsBCo8rSYRdjX/DKFUW1OYOQJe9Uiw/7PWhjR0XyziBh6/CATKeEUrDbzvjNx9/w449Czt44R5ikZM8t0aBNSWWUqyEidfGK1ZDvdTCf3ofll0SvXyO6ehU9No0JyxgRIIWgHgfUSjXmxqsYO+mBQzvs2pA4nUypPAB/Mrm2FpHn0OuRbWzTuf+C1tMXtDtHZCbB6hTlGa8GQSicyHJzbhp54wKHpSqH6/vIXpc8S31O4/aQwqzHiTVbB8oBoZTEYYgRlkY5Jpiss725TXXv6I+Oxz8ItImMYLHrHDxeDrrYYEAnywkkxKrmPdRdl3thAn7SjcV6BFVYQbfV5WCuQfPGK8ivvySMI2yaYAUYrV0l3FhEagiePkJri3jjbfI4ZtTh7V5bkN5FxFUCZRgzPr9Ic3qOW2+9w+7KEitLL9jaOaCbpGjpfr8UhVw4N8ON116hPr2IjKojAg6+AmYL0MbTMBEODd/aRn11B9naRIQeDLI4HRttSAYZHSHoLS5ymFuP1ALWgSDSAxee2OgWKuGU0bX1dWFRVNNO75AIyoSgDUEoqZUV41ffIE1zrEeuhShcibxYMha5cUB14wnlvq++qT7R7bMk0zv0N9bJPXhifUUK4Up9Yn8H/cXnqHfeQ06Muc8skn4rhsmmwc0PEYQ0Z+Z574djXL1+hZWnz3m+vM5Oq/ddByJPwZMIlBLMTDd59dVrXL1ylUql4dq+Co0SgadYeoDFgx2kOebhE3j6GJsn2NCVK6xx95FqQydNSWslxKXzDIoFCYaJ9rCFTXiHLN9KVAygZTSfTvsQPjgXvnVIWumFkw0yyUgev+T4029JdzcohSHIwCVOixNU372OutCAUIBxAJ0OFMYaVGKxL9o0zo/Tu3iJ7UePMCYnk4LUWGpKEoNT7Dw+Irv7DdnODqXbbyCnJp1yvHvQw6QCr6Mk1YgrIrEEUZlASjfTpMSVjCxFn9vwqXliQAFykOfY3X3y+/cQ+3uQJmD0sAqNcbaXWZ7RswZ1/ixmbp5chI45Zgvoo0h+RiBNsc0IRomR9fbopz6OwoOJQxDO36OvMCLwNqFOz0JIB6SCTzDscH8kDAJKpZhOlnCUp2yUqwRnzyBWNij3BgjjdUashrQHWy8xaYrZ2UMszKMW5pATY+jAuTQgFSJ0VOBSuUa9OeFp+yPwWUjhBDsDp/8gReDmo08IAgvZxh758gZ2c5vw6ACZdjDStWYKY31iZcgiRX9xhr1Klb2jXVIJ1VKMNpLBwNBpdzF56vQyghBjLEmSEASKQb9HFAYEQYAJQoJQYHL3zmdZ5qnZFqM1/f7pgjZKgSVl82CD6mSEqkhWDzdBatp7CaKk2O8ckmIxoaIkJEHskolOv4voxxy9POTgOHXVxMMWtWrMvg7pRRFjNy/Te/KMcHMdhhaybgJEDx6S/vUvMX/2C3Sl6quQagieVmtVfvHPf87YxBh//ctfsXew7cRqwScwYhjkWT8fhQVVqvHq9z/kX/75z6h7W+ZR6IpjJD57TvzXf0MpTUia40TCaXiZXGMR5KUKh6/d4GHWYT/vEcsStbBKvTFOOhggyxNEUtAaSIKiJSlJaU7WkUgym/Ps+ZIT/E1z+t2El6tbLJxpUM5CF9Cf0mEpmCkjRoWww23M2X5Lt28HKqRg1A4d0XBV5dX1Te5OTlD58H2Cv/5bSp1DhBiBvUJIIm1Qd75AtNrwv/kXpPML/jNGjF7pHf1mF+b5F//9P+e1127yu99+zMMnT2n3exijfUvUCWAct2IqFVAtx9y6dY2f/uxHXLh0HeVp38MtzI+7sJZ09wjxV79h8ss/EGQdUBEykBBEmLyHCUNatTq7r17jXmK4+/gB3V4Xcuvbzi0qCum1Ukoqdq6FNQUl6HVb9MyApNdjbWmFSq1BMujT77do1Opkg9MFbay19Ht9IhUiVUCWmSG4Vy4pLszOs76xQy+zzDaabL9cI8s0r9+8yXi55NvntV9/ndOH9WztYT7uwQGDJYhCqlLRWzhLWo5o3/kEtbaDzS3dUhkdliEx9KplsjQjNpZgdx2xvYr6pgaNKezENExOQbMJ1TKEoYs1tUUkObab0D08wrb2KW2/JOsfcjxokeYZ+yajC0gZgDXkvno7FgVMjEdUb1/m+PyrPFrrcbi+TE1mCKU8+O8p+jhhzCLpKtaWXGvHGsE6s4c0JyHHyIBmY5yk18fkOTrLTrW6b6yl1x8QFkYHPgaenJtBpS3Wtg9RpTI1rXn++D4617xyfp6y9C3NxoOqjr7rBYu9+Le/N2ssAkk5ChG2Qe/KbZIwov/NPex+h8wq+pUa5K5NqRspTJJT1n3k7nOC3WXs/RqiNoEcn4TxMaiNQbXsdP6k22tMf4Dp9Rm0jkiPDykdbEG/y2HaI7U5HWM4tgIj1PC5h1gqUlGplGhcuoC6fYuXnQHPHz6hXCsRYSFwqZugAHstwjPTjRAjTUatvc2C2w/yJCUnxYYB9XqNTi8j1+7n8sHptg0bY+j1uw5oFrhxkIKxiUmqUxXW1jc5TCzVcolO6yWfP0h55eZ1FsdrxMIXqoRraVEqBK0LRVBXoJS+aQKnixoKiSmNY155mzQQDO6/gMMBBkWnVHZCudrQq8bkaUaEpbq7hNpeQd6vY8emYHoOZqawY2PYRhmiwNl04pJu0gzTT8iPj8l3dwm21wn2D0k7bfI0pZtr1q0T/vbwuXsW0tmCV6sVxq/fhJvXWNrd58XqumNGRZHTHrOgrCCwAnIDRiKtHLKltTZOL85HqTbL/I9JorBCrVLEZ6d0WJerGeMJFWlGGAWYWoVnO0cI8YRbNxTlxjSCqNhcGCbPQmIRaGPY7/TZPz5GW0OoQghCttqG331yn3/y4zLTN88gtKa5vU1jcIzqDciTPrl2Bji5VZQSS7S8S/JyB3npGaU3bsG5C2S1MWxYAu8uN+xjlcKBcLZgIAnvElig4c40AG0Qgz5y94DBwyVa95/SPjygZTNSY5xWKNYLjUsCISlFJcYXZii9cZmjsQaryzuIg2OaxqDy3OW42hYP0TNhndadEYAUiEA5YW0EORnl0DA2M8Pm7u4fHZJ/EGhTHkhuPa3RFgn7pQHtCU1cDYnjUoEwYFG+1xrvggL4JKkYR4OjBx3s71I+dw6xu0/+4gUhuesxVKPeVGGdmJZYekqORN28iWg0TrTNKL8QW98DqVwyawTaWGRUZu7qK5y5cQuT9TBpzw2ekpSqDVRYc+rQEp9oFMJLrsJyohfDVSVyDSsvUY/uEbU2na6CUm5yGidoMujntLOc/tl59qMquR1JntqivZNR6oNPYF2uJsi0dr2q1otKnWKeKKyFfoJUAQhv950Jsn4OIaPJLLxqf6EdMUhJSxmJ0GQ6xyrQB1C7dZuk3UIdHJMXjAkMxvduWmuxe7uYO5+h3ngLMzPpknPj7ln7Ryw8QFXcr4rLjC+cpzl7hptvdjjY2WRne5fD4w6D1NHVgyBgrFFl8dwZJubmiMu1oRCdMMZVHKzwwJv7t2gxkP0E8+ARYukpOun7aoVjEZncBXrdQcJAQnD5Ar04HgYmDhiQFJoB0s83V20ySL84aOllWP4x3IfEKAAvRGItYLMcvb5P+vtl1Mo2prfNoWkzceYCRoZkKzvYr7fI1zeZ/PH3CV67igj8fUmH5AujEbnALPeYvXCB7FzK7sYye2lOiqWi7BCosoDMBpi1VboHx8TXbxCcXUQ0alilRqA7bparoeOLW8hVAUAIB0u7SsZJuGbEibPGQq6xR8ckL5axa8uUBj2S/gBjLVEUuMqc9tooWUaKhfMLmPNn0TJw89+DeZ54S1HZdJuMKRBmsJ7K6M99yiNY3N4QsCnmfmGBbnHrgVJ+dbDmhGOOo3sL//IIIQijkLgcM8CQAQe5oTrWQPZT5O4B9PsEeeZ+xxrUoIvYybHdDvbgALO9i5mbhblpVKOGCAOMB4+EFKgwIqRUXLavqojRfVi8WCeIzKCP26Sbe9i1DeTmJvL4CJkNsDFkpRgbR5gcjBKYUsSgXmFvfJx9K+hkGVZZgjSj2+3TMQmddocwsNRkxbv4+LYqazA6xwaSPM/JUo0QGXEcuc0w94GMMI4paU7RXQFI85y9booKFdLmjM9WCJSkO+iSSkm1WqaXDJiZnUKnXVpHB7R6LQJlaXe6hDJid/OQqNqgWotRgaZvYNDqsTCRsznoEr15m/L+PuWBZ7NYBwAEWiM+/gTCAPHzPyMvVfwcci+oRRDGET/40QfMzM7wd7/+mCdPH9Pvd9GeMTVkeAlJEEQ0pud454cf8CcfvsdUpfIdtEYIEMaSrr4k+F/+mtLOFqJacey4IMBmKVYGpI0KrbPnWJ2f4OX6U3JpCU3E8V6fQCgmL8zTrE2xs7NBGIyTtNvkJkcp6LYOCWQJS0gYhRwethBKEZdicq3pdjIi69qTTusQQhAGIRZvAWqNq8gLsFa7GEFrgiAiVIpcOxDHCihs1y2WfpZw98FD6u++S/C9d5n5+A+Ukq7vqS8eokbqnMqjR6j/ewv53/1T0us30WHss7AiuXQrTlSKuH77FS7fvMLGyhoP7j/m5ctNDg4PXHuLcFXXShwzOTXO4rkFrl67yOz8GaK4Aif2KvBrqwCR5eSrm6Qff05l5TlW5ZBqRJogjCb3LSSZhf2LZ3k2Mck3j7+Fek4oFGEuGWQ9knYfm1giFVGqxFijKdsSNoNBnjLodFBCU66VaDZqpJFAZhm9dssLYJ/eIXBFv96gj1AK7fnKwtOZGs0JrpWqLK+ucdBLaI7XOdjb4s63OW+98gpTtQrKFx3c3uXAM2sd60MKJ9prrEHiBGJLgSKIQ45nZpA/+QnpF1+hX6xSspLISsJAI8KIINWQDdChIjcClbYQ24eI7SWEDEHFWBWCVCihvOC6Kzy08j6DsiSSmr0s4cDkGLQTxJaKAK/JIgTjcUBprk705lXakzdYednlYPkFJm2hK2W09IA6EhkEDuAwGUJItPDN+tYzjApwz0BkBeVSwPLmDvVqEyEVrfYx9XJhzHA6hzWGfq9HHoTDfdAgCOIq87MTVEsRz5Y2GcQVqqWcpSd36SYZb106QzMORm47BaAqrdOINAIhnfuMsdYVJq2lGpdd29TlV8maTdIvv8ZudinLEEOODEJsqBD9AVhIKiFaQzg4QvQOEXsvXA6AGztfTXTFEeNc5/o6oxMpehXJQajZzxzzWHgzE2UsgbaUgFoQUGnWqLxxnfTcZba2j9jZ2SQdtCjXIoywaJvjlBLduqVQCKERyuVWLoUx7nKcAZ5brLSmHEq2Dw9QcQklJUeHPSphhSg6XVFwYw29XtdrRhXWygLCmGazSb1SZ+nFU162OlTLZTq9Lb76tg83X+Xc9BjlQI6SbZ9LSjd4WCH92mG9poxEhpKqkqAEg1e+T1qfpP/FXUzH6VtF6QClYnRUdtp+g4REQR5agv4eUWsbsfYYVIgNShDFiChCBKEHfDJslpAlCQc6J1c5Fd1noCWbuSE1mhxL7rWvlNEIq4mFoqoi4rE61ddvoS9eZvdgn/Vnz0hSTRyHGAHaOskIpZxWD3nu2SBuo3H5h5exKIJq47yRAiNodTLCSvVUMRvwRTVrieOQemOK/b0WpcAiGmM82z7Aykdcv2RoNGZARW7fMi4Gx+cmCEmrMyBP/bz1bkraWtb22/z+k6/54P03mXvjEvZRicHzJVQvRecBHaVolSIkyr2/1iAx6NUWnZefEM48Irh8Drm4gB2bwJRK2Ch04yiVA+D9M3Ehvgfg8hSRJMhuD7t7SPZ8HfNinezwkMyk9KUrVDuWtgNsDJJIBVTLFRoLc5Rfv8T+WJ0Hj9dJ2z0mi8Ky9MUv6wuyvkNBSYWwCmGM0wstqJRCYqUizTPKlRK9eu2Pjsg/2D1K64R+3saoPrGKCeMSmsCtU9Yg0M622kf1Q90bBNIWXhNexyVN2NnbYe7yJezBIXZ3i8BIjBIM0JSlpCyd8CJpgnnyEN1qI19/Ezs9hfEPxp3KV0I8IGL8N1TobtElHg3i8tgQDJRSedFY6wPdQmjRJ9l+oVC+5Uf0EsTyOuGz+4TdA4TOHYqtvcuQNeSpodMb0B+rcjw5Tc96IbTC/QNG1mgCMI5e7RBydw1apxjfy3j6qaJFBsL1QhonCGiEISNl0D4my3OECAgqZeJyCSE1aeeQwfI6xBlmqsxgkKPiiLhZo6Ia8Prb7H7yezCQJ30C6YEE4Xr0MQa7v0v++aeIV24hFs9iA+XEa31CIN2lobEFi9UjlYaoVGb23EXmLl7GZO6lF55KGEQlx87CPTsHDPqU31iv1TF63NZo7OER5uFD1Oaae3H9rLQ5ZLgKQT/N6euc4MJZBuMTGDNKpF0blBm27MgT3ykqs8XsF8OvTrk9Ctw1WOt6lW2OsgK9cUT26/vUuxnmzTk2H+3QSQNqc+OIoEy31SFPcuYmy2SfPkQd56g3F8m8oN3JM4RWEexKzixc49jmdJZX0EaTWUlJuD5X4UEpaQ3m+IDel3dQL14QX7qEWphD1BoQh1AkCjDUYnCbeEFldi0vzqXjBBruFzU7GGAPW2TrG+iNVUT7iEi4xM3kXqRVOeaRtgZtLblSqMU5OHeW1CdkGFfBKewAnbSy3+dOah4NmVSja3DHKb6PxTz3oMvw31EajcC1HFhlKVqNhu0UHkwBHNslilBBiAwyBzqmOTtRCTk9idGG+hGUehB65onAorI+9jBBHx2RrW2gxydRVy+gFueRzRrEMdYH9gSu6jRkZxTz3FiXlOa567FPNXT6mNVNsofPCA92ifQARe4qkDLEhiEmDklDSKOAvF7haGKcdRly2E9IPLjUH/QxR4I00STJgHqjTEWUkIFrJTVakWYp1jqtAmssaZKSZSlhMIbEkmeZo9r7YCFSp6sTFsiA1195jSw3PH38iMXpRVq7G2wctHnzjXc43D0gDvt0+h2UspTGmkRIWgeHJP2cZL/LzMV5ypUy5Wqdw+2XhOU6wsBx0qLT6tI4e4no6lXCh/cJ8my431ljCNIB8te/ZTDIsP/0F+haYwjF4N8hpRRXb15h8fwiy8trPH66xNrLLfqZC9TDMGR8apLz/z/a/uvHsuxK8wR/Wxx1lWkz1yIkGQyqZOqqrqpWaPRg+qGBwQwwf9+8zMM8TT90A12DEpnJJDOpMsjQ4RHhyrTZ1UdsNQ97n2vOrM4EWG11gu50N7dr99yz1Vrf+r5vPX3AO28/4e7eLqW+6XbTX8F73IuX1P+/v6K4uo5Aexcljk5opG9jS+h7D3j++IhfPPuYc+bs7E+YP19inOfqqiHTM8RVh5GGzllm1zVBwPBgwqAouHh1jMsm7D24Tz4YIlzg6vKaVVPT1J4spCLJLV0hBJy3KCmRKkMIjfMOleUIlcVzQ/CG2SIEEc92KUFIicdT15amnvNXP/8lxZ/9BPWTP+Lw739J1sauiFKkzlpRq0j5+hXi//X/Rv3Zn2L+5V9iDw83Mu4QbuTfBNBZwZP33uXxO+/gnMGaWP0TUm7AXqlSm+p+9FOlbAMCCYHwHnF2RfbTf6D8/GNEYXFDjQnblN6inEU2LTgXWw7fP+Ls8SP+7tNPeXl6DDnkA4VpHDqXBAtVFcEs4zqUymjbaARqQo33HVprBqMR+WDAYDhktZyzatpYQb/FS0pJlkk62yHqNSHJmoQMcb6IwGR3i++PKz79/DNeTudUgxHT61N+/lHDn3zv+xzsbG/ikf5IjDMtxbEiehpJIVA6ix5TCLaVZCUU/NlfYPe3kJ98BRcrtIjdVMJAk+kC1QWUdQib/GSER3objYhFjM28EJtCTAt0zrEyJU1ZsLItJYJBnlOoaPNKZyiUpKg0o3cOqL/3Ad+GQ9Sza9rFa3CLeNbqgNUi4tYyMeOFQGpN6Lr4OYNHEltVixBjDCUVmVSMByMm5QLbLDnYP+Di6oLL5RJ/i+diIBpXmzzKDoWOB2XwAakG3Hv0HuPRgI8//oq5yxlVipNn/8CvTc0P3n+brSqPjWj7Lgph41YHhOhZIpOtQqYhQI5krBTro6fIfzkifPwJ7tkJYWWTD06GGI7RnSFvDZl1EQiM9HpEsAjfpGIxm/UWBFgRu1KtnIJOs2osWZBUZU4VAlkQYAyZDGS5Jr+3j/rh97gcbPHsd9+yY1bg6k1+I7TCJ0BKioDwIrKbpUIag3I+rnHiGUmSVAoZ/z4aDOiahlnXcnC0x2w2Z7qckVW3x1zsn3Vn2zSnA4WIJTDrAoGcamvCBx8OGX3xEV+cLKiKIV13zUeffITxH/Lu0S6VlhH8erPQHWKQLSJ1OxaWVEzOBTAsCpTW8PRDwvYY84vfoo4XhNaR5Vn0DSs0SmsEgawzZNYiPQhhIyO5WyOaPneQqBDf1wsPwWGDYD0cUA8nXCzXLIJDCUWWSQolwRhyFxgIRVnklA/voL7/Ic3WDsvnz7k+P8G0K5BlPDsEOOFTnhrb1SMzRAiY4PFKpbkryDQ4E5+GUgotNYNKs1zNWM5alLrFcQwpdyZQ6cCTB3tU5Ranr0/IREO1tc3np3O69hO+/27DePsOIquIfZIUQsbOlFIJ+uYkPgHRAC51T/rmleXoq+fs/dk95A/exVRD/NffEuYX1NJgM0nhPEbndFIj2o7SWUrrkM+vsM8voPotcm8bcbSD2tmGwYhQFHidmLGBmIsai6sb7HyJu5wSTs/h+hqMwwfP3DkuRGwbH1LCHjkdijwvGI1GbD04ovjgKfPJmGdfHfP6m2P2dkag1MYfUqaKbCC+d2xtryBI8OCcow1mAygFE7v9uXbJ1uCflir+Yd2jhpr6vTH5nbtMPrd4bXAyVdOl3KQafY0vJrf0hUF6VgD0SXqgW685H2XsfvAB5hcrQr3k2gTWQXA/9/SprwiBrHOI58+wiznhve8inj6JWnpS4tN/eIjVJ6UicwIgiBQoJYTgDQZLCGKDXPbtnwNiE68KFwinF8gvviQ//TZ2hhDRWAsR85TgPcZY6rpjPShYPX7CQuWx6i96xLFP4UnJYHpeGxQ1yke8CYQ3n98tXl5ITFHEjgHVAGstQQYyOYjBmI3sJDWaoMoSb1oKY8jyivKqZrgO5CuL1oLRUiCuHMW9+6zf/S6nv/kNdBaXTPqCcCgRUCL5+VxfYn/+Mzi7QH33feRkfCN3IaHTIlW5khZdKYWUUUKGiMn9ja9KPKiS2fdGJrKZA5vEJQbltIbw/Dnyq8+R06vk1H4jsotO39B2lrWxhHt3sI8fYURvshzvUxJSI6ObwRGEBEomoE1sOEub193qtaHwx8UVnINlwPzsc7LXF6hHR7Q7Q1a2iYBOJlBlhtAqJlc/fIu8zfC/fY1o18gf76fqffrxAbBA7SjMgKPH3+XaC05fPKfpHGWqcvTyR0eUAMrOIc5e012fI7/YRh3dQR7dQe3soIYDfNZ3moqfoS++ECKgFjtBCaQP+M4QZnPM1SXd6Snu7AyaJSK4aATad5RKz9gFh3Ee4zxuWKEeP8AcHWFVHoGO5GnUa6Hp33szPjcPQPTV6RA2kqvbhk8JsXLUt1SXQm4kHwnzjgfAG3sTqRXkm3R0gUKK+CuaL0bGlPdwNl3SDkqawx26UrF1OWUwW5B14Q09b0AGQ248/qLBTc+ofzNE7u6h9nZR21uInQlhOEQUBSLTEVGGZHDs8csV9mpGe3GNO7sknF1QdWvGdLEC2IdcQSBcpI+aKmdVDqlHA+bVgOOguJ4tsbaLLBDj8as1bWNwPnaMyhuJMRZrDEHG9BZncU5grUIpTZZH8M7YBi89xrZ4omxHSElZ3G6i6LxDFhml6NAi8PnHn/Dy+SvyMueL3/6adx8eUbiGy66jk4r7j5+wnY1o9+Z89PGv8eUI2Xacn1wwONiimS6wTnL/4WO6+YJVu+aLs5eUf/wDitMzdi9O6XeaKJkTyK4h/+u/IazWyP/L/4A7OsIL1R+G8UQTMBhVfO/D7/Cd772PseZmZxIxuMpkZKpGv7DNTIyV0q4l/PZ38LNfRLnkaIRpazLboto1EAhSYSY7zN66zyenx3zx4pSVXbGYL1CuI6skzXrNdJVRYxhvl5hVzWq+YrFs2ZWKQmpWC4Ov5mSlYG9rQLOu0Xf3ubqes5ytWNbtrUoyYnFGomSW2JHJ+yc1WNCZioxTAq0zKKUioONdkhTH1t0inV/Xszl//euPyH70A7A/5PDXvyY3DQmZZsPoDZ58PUP/h3+H+fQLuh/9CPP9D3F3D6LvmJA3wE1f45CCTBZkWXFTcSUVE27+sjlx4tkY4xpxPkX98mMGv/gV5dkLNIbxaMD13h6+GuGsJV/OyIHOt8zuHfLsJz/kZ89f8+LsFc5bwjrEZNBBJguEElRlQVUWCAvr1tJai11bgvPgBOODHYaTnahsDZ4gJD6k4OkWLykFRV4gk2yn9R1OBGSuELlm3RmqckhV7fL97/0E/cXHfPbimGJQsV7P+Jvf/Jofffd7PDo8SsqcGBNYH898qcDKePY3FlTwOOOiOSgKZwN1m9Pe+Q75wT3Ul5/hX7xCth25hXzdokz02OpjPBli81wELIKl9p6w8WGBtQ8sCayamqzrOBCwleWUgyqyRawlVAr2R4R3n9DsP+HiEr757GOeFp5cRXDBSh8rzzqDNiZNNkRpgDQO0bko70/rSisdC2TpPiIzVlBVBbUMyEyze3TIxfk5trs9aY0UsQjU1jXWB0qlGFY51jqsCYQ8Y/foPX4oCn79299yWmsm45LT11/xd8bxww/eZ29Y4ugBFEHf+VfLGNZIBNKAsG4j5wxB0BnJyu3QvfcTqgenqE8+Q1ys0K2jqDt0W6O83RQVIZV5XAzn1z5QQ+q6GSuvJsDKe9ZNgzAdWwK2dcagKuNZbhwuF/hxhXj3CeHxI2YryfnH33B58pzdo4MIUKT9WAqdCjwShEtz1OGThE2kbksQW54rIVlJg8wk1kNQkqwsyWUHKPZ2tzk7u+Tsn1Zk/GddAgEhsGpbWiRVXlAOBqw7y7Jz6AyKao8H73yftfmI3768phyP0W7B55/+Fm++y3sPjlAeOh8wLrKTfIh7oEpzUzmL9UkC7WMx1YZA23lqeQf5kxHZydeET5/h5gbpNHkQlHVD3rWo3jRapEQi3XtA0AUwIcnuiLGgCWC8Z1k3yFaiOsMdnTEscgotUd5hcYhMIba2kG8/Rr39Dlfrjq9//Q/sagE+mS5Kic519EyzfQ4UlQh4jwgO51zMc+KNxQInSSadwOMii2CCqe1GPn8b15s+pMIHSiV4eLRNmRe8PD7FrmcMR1s8v14SfvcFH7zbsX14H52P6fUjIRmBT4ZDlNY0jadtG7q2wYdY+B3lkp2tEePBAF1t0Q0GtOMB5stvYH5Fblt06HBK4qVEInAh4HR8BtqBqmv4ZoX75iVOisj8zSJTitQoJziHsdH2og4OlWuUFKyEY4HBhkAjoE2JiQgBBWRCURUV5d4ug6f3UI/usJCa02cnzM8vCHSpkK+SlC29Xulo++FcNDCOVW48YEJAOpOK/zHe6tYtSgpa1v/kmPxhRsQyQ4mS8rvfoTi5QoeTdNgkHxTeSG4SHdf30z9+cbOQ+0QpIKjrFdPhiJ3vfpf1P/yG41UTDXlDstfbIKsgvUOdn+BnU/zr18gPPkTePcRrlX569KGQgnhAEZNBUvLj6CvbyVAw9K+5maY30FeAlcF++TX6+efk9RTV1rhFDYUm5DKZ7kqsC6xrwypXrN9+ynU2wiXtIT5V9vsHRKz2B9+n+H2EFSe3dy6Zg93o3G/tEoIgc9AC46BZrJAyoIVG5QVKRdCGvACp8aEGlRL+xiA7S9ZApi1ZZpBdSzurGQ13Wb31lOvPPkF6TxMks+A5yqAKASUC3gtkU2M/+wR3dor67oeoxw/wOnUI6GGQcJOgolN1K42Lk4HeoFam6bFhK/TDl2ZWH3TJIHBnl5hPPkFfnaBsR7tq8caRDfN0qAsIHuMctbGEe/vYd9+ilToeBIiNDxFpKDdWu0lO04M/0b8lydsIePGGGeVtDqWU9EwRvKD75jXh1Tnljx5Rz2oWP/0talHjBhkyNV6M9C2FzTPEd94GWdL87e+Quw41GST5XkB4YpWrddiLmtzB09F9th6XnL96xqpekyUaqBCR0YRS4GK3EUxHuD7Hza5wXz/Db+0QqgFyMoLhCMoSkecEFasM3jqwJlL2V2tYLWEZf9nVAm87+rXpCQQvUtIu8L1/jRA0zhJGQ9TTJ9iDA5zOoB+ZQDIg7gOK31uSxF6ePcAaUmE67leBG8r8bV79O25kkOkNfJJw9XtnTKT7vSBJN0nbrOj32hTU+L5Tg8d7y/m0YV1q5pMBB+OS3esR45NLytUaHd6cmw5CBFql1HD+Eo5fRKBX57gk1xRKb2SpMSlzCGPQwZLhKHBI6ZD/ybxP+53p8FqyziecjUacC8HVYsl8XWN9HKe6a8kyRZErirygbQ0ihuG0TY0SUOhIxRVA13YIISnKCPxKIWjrNbZzmM7ggoPgUUqQDQe3OoZt07G8umB2fsX1bMayqRmMhpi24/xqhpUNeqQxLmdvdwthaupFzfHpBdXBFqpUCCfJBxWnX5/gnKdoHaoccLS9zXq64Nxe8E05JP+Xf07+b/8Do+UM6U3yiYsTUzuL+vWv6E5PMX/xZ7jvf4jf3iHI6JeQlIEgQAuBzPIY9IVI0w09aJnAyj5bk97DxTnir39G+YtfUTRriv1tFuMtbDshLK5Q1uCtp9nZ5+SPvsen9YypWXJ4d5vLpWAwGuJWa+4e5ZxfzRnvlFRlhdKBpXWs1gYXFNJJ5lcLZhdrWilYzVuUkzx8+pTdQUWZK+bDAafHlzcI8y1cQkha6wntCqWi3KkqK6QUMXmVgta6yA50JhpCeoDYyURy44HTm3uenZ/z7z/6CH7yI3yuufOrj8jrBamkm9hu6bW2Q77+lvzkmO4//A3NkyeYP/8xPHqEn4w2xbGb0+3Newc2+1m4AWxEOrNMhzi9IvvVp5S/+oji+hXSNwgR2UOZ02yvlyy3Jhi5Q+UsNs+YvfuYL7/zDv/+62M++epLWhf9mUJncMKhBxm5F5i5oa4tmcoZFQNas8R2luBk7BTjPFJmXF1OMa3BeRfBoTzD2duVKgohUbrAe0nXdVwtFljhCKpi2Wq+fHXNossYVQqpCord9/Evrrk8uWCys8v8YsrP/u4XnD79AK0ruqbGGkNnLNale+0lo0oDAlM3sXOdi/tt8BZvOib7uzx++l32Hj9GfPM18stvsa6OgCDiZtwScB9C4MR5rgGJRyVQ3wSwQjAAShHYKTKKPI8MKw3ycIB9/JTZ7iPmbox8saC7OMVeH9PtTKCIpvA+9L5e0ZNH+NhNL/gQE/7oFhvBBq0ja1MIBItNwhOLI6CKAusMOtPs7e2ntra3NooEYLZcUHeWbDiicZIXJxfUrWdrvE2elwS3QxgcMHv2jLYZozLBt88+YbVq2NvZwxuDaWucjYCGdX7jc+E9kSUVHKZrcM7jHARrYzFPaw4e3efhh3/CbnuB++prmm+m5HVLnhIylWSRIvQMesmZs1yIQOpNhSRaPxkkmRRkUjLRiiovCEhMCIjdMTw8YnXvPjObYV/MyK+vUYsZwRhcZ+mDW+sNzotNHuWCxzq7YWJEM+kYvygpyHWGDDbGSQic6aIvioh+TM45pNDs7W5zeXl1i2MY72bdWi7mK2wQlMYTrlYsZs9ZzFZsjbbIi5zgHdd2wHr5knVjGQwK6uYlv5wvOT19QiE8xrQYY/A25UZBJIPeKHmTmcabDtt1kWnt/CYuHOzs8uC997j7rx6Sv3qG+Pwb5NUcbEjG42/IR8MNUFGHwLELWBFZZwAqQBckCwK1s+RBsJVr9sohWZURsLjWobcnqEcP8W+9zXowYHlyyfLFc1ZnZ2wf7MUzoC+8+BBZWy7Gej5J6rxzqY28Ax1N1qWQVEVO5wPCxBbqsZgASIHK9JvQ/f/5MUyyNillbHZiPEUJD+9sMxiO+Oyr50ynZwyHA14tl5jPnvFBsBzeeYLKxzEHcrERy7DK0SojhGhM36NBSgq2RgUP7x8wGVVxLk8qxHceosYDumevCK9OUetplBEqgcozMuGRtkU6Ey1UfALxUtQZrI2A9ob5EDanp/LQCbBSYYRg6jwtoLJY+MwRKO/JvCPXOUU1oLp3RPbOQ7rdbV5fzvCXU+qujXt+Kkp6AY7ohxYkCBVN3Hs/X0KSavbgYALd+nhLOo8OkkX7T3u9/UGgjVgZ/LML5L8CUQ4I62Qqu2lYfQOC9IBMZNQkRsAmoIjVJogVgGAdy/kcsX+Aee97rH7zEXnX0mSSkRQIFzZBZwgxUVDrNfKrz3CvXuIevYX+8EP81hYh131qkICaOOHeqCnf6CJ5M+DpgSXAekTd4V+e4T/+B4r6klzb2ALSRskO3iN83Py9MzStpdaC5v13ucgndKlFZn8o9z9bEDatOAMhJmd9hxpI8ocI9IhEKbvVVDHEUfEdmJML7Ok5ObF7gag8Ylwgit5cMUralJL4XMdNH1A6J3JfJN1+Qdes4atr9t9+hHnSsXz+LVcu0IYQ9XvpdX3iLL3Bnp3QXF8hnt0nf/895MEBoSwJG4wqjckmaY6BjkrGVpF2JpIPjrypQvobdpc0Bn81o332DPfqBcqsE+U3bZfOgo9gn/OxkmQIcPcA99bTSMN7QwzpCJsDevPlNKvevGIVJrZK79Fxf4tD2F+xa5SJDvWtY/XR1wy2BvgfvoUylvZ/eYmezdDFAd4EjO5Bpbh/+CJHf/iA5pPPMB8/Y+dPPwAZA08RMgJRZgSSrCiQxy133ZDtu+9zevYlzXyGkBItYwcKoUTUiqYAtB9DuhqmBnEN4RiQyU9JKbwU2BDwwaGdjwmid0j8zUP2yYskJEO04EHJmGh6h3GOzjhcAHG4h37yEL+zg1d9q+d/5CAVRDyANl8NSZqY2rz+o2MviN7I8PbHsL87HyJTKQj/BgiemtKnrTL4ZPIdqTnx+aY55n3cl7xzWGfpnN3MzRA8y7XFWseqyFkc7HKwNWTn7JLyeoGuu3jo9ZXDEAN8pIcsIIRHB58o4T2gfWPMKdKn6J/aDUh/g0J5YlcLV2jarREX21u81AWvF2sWtovVdzxN3eCswzlLllUoKdBKEDKBVhlFrsFZurrGKxtp0ESAsak7jIkAve0MwVu6zrJa11hngVgpHlS32yXDec/p62Om8wXrpuXO1hajYcW1vaKpG16+mjPcHnLn8V2Ut7z49hVt3bGarym2CkQLuVeUkzHu1SXrtUGXgZMvn5M/CdiupnMLPvp0RvHhH6H+/I/Rf/szyuUs+mdtQG7AG/IXz5GnJ5i//TndH/8E+YPv4ff2+tM4jVUyr0yod9yz+nUbh054j1zM4dcfkf3t35OfvI7zRErGqxryAjfZJrQGkQ1pnxzx8u27/KI+57ib0xYN0+kFZBkhF0ifE3RGXuVYb2mbDrv0eCsphoqdyYQ7R4eYxRpvFK3tKLTCB4ksYTQuuZp68oHizqMjLk+ntzaGPgSs97HzWJZjnaVu2gh46QzvLCLTUd4XQmxZ7TuUrhgMB0y2x/TVzwCJ/em5Xi74m48/pXvnHXz1Jxz+4h+oZhexKgyJtR1u1qrvyJbnqN9d415+DdUQ89YTzPvv4idb+MkIXw0geQgJpXqYGeF97LTTGuRyDZdT1KtT9Bdfk796QVFPoyQan7w0Eu27adGDMZm1rEYjjNzDHu3wxePH/K+/+IS//cUvqNs1SsHWQUU5yRFBxW40XlKvLdeXF8yGJU+fPMCawItXpxhrKIuMndGAv//57+is40aqIt4sbN/a1XaWr4+vMKbDO0PbNgQcJ02ctzKTfPnlM0TIcRaE89h6ivM1zXQBCGbec/r8JVpmaCFTR7G4v9001ogeX1JrgjWRYUVkPCgJ+MDiVLJ8vsO9p+/y4N0fsPP+2/iTb2iff4uar9BO4OcG3aV4RgakVpRVFquxC4OwMUjXwDiTVJVGF4owVITDMf7xXezWAz6fZnz9yQlH4YxDDPgWIcAEi0sHiJYwKSTDXNA1ISUlsfkFpQThoY5xC8TkQm4M8EHJDCljh0EhNW1naLuOcjQmu8WW350xvDw+x5gWH2DaWeZnl6gi4/TVC4SXBBvBJWdrrG1oVzVCCLx3PL+84oXUkfnmI1gfa1GJZRsi4KZ0NCsO3iZpLRBC6jgFr2fH1Md3uPPoIfe+/ycM3p8iTp7jXr8izDuCU4S1RRgi81cIfCbRpUIGAUuLCCI2GJGBSimKIkPlmi7XqKMJ6tE92Dti5XP++qPPsIuGJ1XGNj7uNT7K03qmuZCBrFAUShBWyZ5NSsgyFB7pFL5Layxm9CnujmdylmlyJakRKKVo2w5nPUU5YO9g/9bGEKDrDKevz6KvYIDrdRM9+vKCy5OX0XvHR3aXdRbrLFKsWF+LBCBeM3v57Y2nn0sdMEWSKyY2qNIanWnwfcSdGnSk4tbi8jWXx6959egtHr/1fQ4evoP/5lO6Zy9Qp1OyzkePrk3n15jdtCFwFQKN8CjPpoTuRfTrnMiMSilGeSwMdk1H2CqR771FePCQc5FzejkjvHzN9nqF7NaI1Khmo9ZxBuELsje6rxlnCXkORQ4WQn0DuErYyDx7I/ogEiAXQlyHt4ifhhDvtRhWUA6Yr1ds5xllCNzZrUA95tcfNczPv2FrnDF18MtPv+YH1nLvwVPQY7y1WNehhCfXUc61AclEIJOCQZWTZwJvaqQP5B6C8piDAj18QNgf458fExYztGnIG0vRNGjb3DDfBKmY2RtWxG61ps/ZiHYHJgSaEGgDNOsWhyD3sJ0VDMocJTXSy2gELQNhNCB7+wHqyX2aPOfibMrLZy/ZVR5ZZbjOJSwhqntcSN5gsi/khwj0+yjfdz4gbEBLhVYSIzoEsZNUVhQUUmD/GQn/H+ZpE0Asa7rZFWRy87WeExIT7J5/EIOW+D19tiNSHvdGAhRIIYtjtphRHOxw9IPvcP3bT5l3LaPk1Nx/BBfYJM/CB3S9IHz+Ebx8gbp7H3//PmF/nzCsoMgRmU6txNP94xHELjk9ZZiEWsqmxV/NCScn+NfPUVdn6NAhdYiPKvluCBEBiGBk7GrhLM1kwPrxQ86LCa1xaZNNLITgCeGGiYRIcp8+PhYQn6JHKDaLMG4ft3sJQJ6ssB+9YP3Rb2hn58highA6fqbDMcWPv4v63tv4cfQiEVJBppJONhBUZBi1tsM/3aFZSurPvkH/7pw7bz/h5SPN+vmzKBXLJJWIevJIOychbx7RNvD8a7rTY+T+IfrhIzg8QE7GkCXDOCFigJmeX9/mGiFSdadHyJNW3zrCco27uMC8eo07OYZ6Ec3QpCCoCCKp9LresLoxhg6BengX8/gBbZbFH52CNOFj4NMHmimsiUOY6JO96XE8VMJGk/4GgeL2xlFEwzKRgDWxbuBihnzvCLk9wVrP6M8+ZHBxSkHG3v276MNdlq9fIWfgunVEuScjwpNDzK9+i1s3CBmfqxAej4v/6Rx9ZxtevKa4MOyHbe68+5d8cfopp6cvaK0hD4HM9n4wbBL6ft3arovyHwJCmJQupnTDexQkC3FABHp/Fx+pPHghsC6ASFptBN4HmtbQuYAfVhTvPEXevxPBIAHgkEGminOg71bWI+891CAEqN4nJo1sSAD0TX8Wv2m7fXuD2O99iVGTvoZ4Y52kK7zhpi2C2Lyil3H2f3PeYq3ZaIZv2sILms7gnKc1jplWHOxtszseMlisKZZrsmWNthbhPHQdLrWCFQSktalCzGbJxbn+BhiWBv9m54rmbVZJ3LCkHVeshhUXWc6JUFysa+Z1TecsiJCYc/1+Gdea1oo8z9A6HvTx+Udfn877yLxUEmsdrrPxKfhooI6PwWDXmQhEioDUMnbGucVLa8XKtYhMgoS2bdmbjPHO0djILBh5RUUgb1uUs9TrNbKQlMOCsGqYzVbYoFA6p2vXsTWlCVx+/ZI8V9Rtx2LR8FMf0D/+E+Qf/ZjDv/sFZbNEOLdJ+/uObbJr0S9foKdT3E//BvPD7+E+/BC2dvCDAV6nLl9pL4tD58FZVNvC5QXhky+Qv/6I6uICbVp6iZUIAeEMefCsS816ZxszGfH6e2/x068/5ZOr1+zc3SevhrCYoStBs66ppy1VJRFOsrhY0nVLbOiQXlCvLI4Fe1sL7h0ecn20pD09YzltCBJevXxN6BoWi4bOOY7u3GFQ3h74JrVmcu8BgUDXRrPJfh7L3a2Ih1lDCB7b1FjbUZQlQpVkZYnKM6KO/w0ZdKJCzxYL/u6LL1k+fMB3/9WfcecXv2X8+gWZ7zbjtam69vcTLHI1RwZL/nd/C7/8O0KWY3f2aIY7CK0JgyGhyGNA6Dyis6hmjZivUNNrZL1EdiuUd0jheNPAUgS5kaXiDOrqktJ0zLKMV+894GVW8r/973/Fbz/9jLpZEwhU4wKZa9bzjizXNAuDaT2ZUqy9w3vParmmWXe0TYtNQHAYlNimo+k6errXxunwFtlSAM4aVpcn2ODwIr6LJETQTUqwmq6bJRPrsGHPRr/hWPmRIYDryJREK0Gp0h4qZfSykQqlBFo5Mu3RUpDl0WNPqZI8kwwHksGgYrw/gbwlMKXcv0d5/xHrxyc001fkZYVsO7rXp2jhYZhzVA1hOKBeWtqPvkIsFtG3rFSErQFifwc/3mGVb0G1xaAYIG2gPvma02e/ZuvONmG8hRIx9jWZpnawbg1VlSPyMVOR0WhHnjfkyQ/J2diZJlb6Y2KghIgdrAIoLRFS4b2gyHJMYm9A4PL0/Fbjm77DX2T5CIRpI0vetRjvCM5tyPqboqtP/kCEdHZEqbGUEaqWIZDpvktlbAiUZZDJaIiqtUQrhc7UpsvPZDxkvD1CqDVCVGy99S7qyRPmJ19jTcdgOMZfnuNOz8mynFAWHE2G3BuNWJ/PWP7iM8KyAyWQRYbeGcPuLmxv4XZ2WcsBZVZShkBWz2leP6N2Cju4S8gLpPAorRBasl6vCUiKakS2s89V19LlKzKXWD1B4DtPMDGQCcnTRghwaa5rpdAonIsd8KJAJ4C3XF1eMdnbucVRjPdgjdmYLce4X+KaGhPcpgOejK2uNqbkERyNjJNAiMC0lLHY5tMZJG98U6Xr87oUByX2gpYCKQNKB3K1ILv4jLm5onj0mLvf/3PKD3+Mff0l7vgUHSRmNsOdLVBOxP01F+xUObrK6S5n2KsWYeN7CiUoqoJsVEKRESYTwv27yDt3oBxSLxb8/c9/znzZ8tbhLlYrfFngM4WXCustDkGWl4wPdsjLkvr8HJbr+LyS56fq4+RNgSx2N+lrokprtNAEJRBKIUNfqLydyyYGnrSes5NLmsWcd+6vGVcVWhdMZMnDnRE/+7KBZs54e8Tl0vCrX31KPVuRD7Zpu0DXdVxfLxgrB8MKqQcILVEyAp6ZKPn4l1/wTfZtlND62CLBOM/2/hZ3Hj1gePdd/IsZ5tOX6PYSb2NnMCHDhrHef3IP1AGmIVAjNpJnicAANYE1UfpaKsV2XjApC7KyJEiNcxKT54T7u6h379FVA2azmubla9xigVgtCAONSqSEvgATfTBvYnofYlMeawPWOVxi28SSLGgpybOM1rnk2CKQ3lEV/zQ08weDNnQWYTryskBey1gx6pOPPqLffPtNJbh3irn5t/jVze8hEIKlNTC8d4ddKak//YzlcsFAyUhZTrTGGN3H18RqkUfVM/h6gXv9DXa8jRiMELt7hPEk+t7kBWQqJfvg2g7tPaJtEatFPByvr1GL6+ib4VKXFQHBJFqpTNTSIPFO0FnLsmtpJiXrhw+ZlRNam9p5keQz4ea5hMSfDn0VesOySUXSED1bpCcGFZvixe0FNqHuEP/fvydfz1l2lywmnuKDBzgPs4+/YHCxhH87Y3TSIP7Fd3GjmCSiwIvohh+0BKkQ1uO+vqBeXuCDQ5rA6GXHO3efYJ7mfPr1Z0y7jiqpnN4gWyXGRzToo22iDOPsGFENcJNdxP4+cnsLtTVBDCpEllq5JbwveI/toukaTRvHbzHHX18Rrq/wyxlYgwyR0hy82DAILIrgkj7VWhpjaQSIJw8xTx5iVKIYij6lDjfzuzerFeENtk3A993Rei8bETZsqoBIHaVu8xIRhBIgixykiwaWkyFojTM1Lle4gy2KlcB/fobePeSt//G/4duf/hxVDJFIvPMM7u4y+1mLaRrUZATnK4IzSSttMNdT2i8N2sTNRq8V5euS7x79iEIfTtEAAQAASURBVOLgiONvv8DO59iuQQFayVit6k0WpUiaYZfw28iQsmkuCOLGLen9p24+o0xU9JmULLRCOUdmOvLORmZUWZDd36W8fw92tjBvLBURZARl+vUWevw9DaO4MQiP52LyqQryDX+iCMj5Hhu87THskxeSL0YCqRJ2w6ZVvRD0ZSbxRnvgCJJ7ehgxeI+zqeMbIv1MCInB1wWLcZ4lsBAwzTXbW2MmwwHDrYZiuUav6/jZVWwHq52PDLb+ntLh0os0+mfSb/8BcAJcprGjAfWgZFFkzJRiClw3hoXvaDpDZy3Ou7jWZJQ8ChEDuCzT6CxDah2TqwDWurTjx4H23m3MmJ11OB8lYtbFCmtIgeHmJoUgK/NbHUUpBHlRoFWGmq8oypzz60t0kbNdZExXS7KsiKDFVo41HQ7DZGub7mrJct6yajpUXjDZ3aZZNsxO5mxNKpzWXCyjnNNax8vjV/yyytHf+SHB/Yj9X/6GajVHih56SfMhzWfahkJA9u/+I/5vfkYYTeju38MOh4jtHaiqyNwyFlHXyMspvD5BXF2Q1Wuks3H+IDb/QSCs1mRKI8qSk4eHLLYH/N3vfsln58fkg5zl7Jo8yxAWcp8hSsngvqK+WtOuO0blmDwrOJudMJ03WKsikNDl1MuGe+/cYfvBLt98+i3T+QJnoasteEFeDDjY3edWSxreE1bRl0cbg2vqmPyhMNexs5UIscNHsBbbrvDLmrIYgjPEjgRyU5DoryAUPggW65pfffYVl3eP+OG/+WPufnnE3m9+x6BepDjhppAlbv7AYjIkzzTVbIrwDaHLKC4uUE0d596bzyBR2YMP9OajyASMiDfZvv1rboA+6z2zUcHp2/f5vLV88/w5X3z5FXW93hSgnPVcvJ7jfWA4rljNaiSCw/0dlJKJIRfNPoUUic0cUBK0FtCFG/CZQDUuGe1WPP+svrVhFAQq5VJiLslS8qalQCsVPfKGMvriKUWWjDK1VuRak0mJTkagWsdKqNIK2QO+eYGSOVJpMi3IMhU72aXv01mOzzN8XmIpEFlJaz1NXbNuogF1NdmiKBy+86hRhjg6xPuA8x4pNCov0buC0f4uwRucyGnRWJkhZIH3mtn5HLVaMjkqAcnueICUHus7QmcARVVmtHjma0OzXBNay3HzGtu2aGBra8jWaEimFaLtUqSebAjS+eJ8Yjjr+Pl88GRaRdmbEAzKAle3mK69tTGUUjApNSqNnRI3YyeS6XemBFrJ6H+jNHmm4veq6A2nVRbBmNRlUErIihyZKbRSZErH71EaqRJTWEczf7KcVhcECpTKWK1XOOdYrzrKQjHePcTVa4wFcfiA8v4DghO4ziMzTaYztrNtRrv7UVosMzqhsFIjRI7wErtYc3z8mscP7sf3LHJ2tkcsz+YE5/BdS3COMs9YdB3TxQLTOmbH56xOL/GdIc8023tbDMsCZdxGdhfj3OiPqZKhukhNDIa5jM08pATv8HiGRQGm5eT581sbQ4j7TpbOISV6gnCUKMeinAYhIrOBG4NvJWXs0iYiuJZncX1Fz7+0XjONynQcY6nTeEpkArlypSnyDFFm6GqAHoyohgOCt9jOMChzcj1gsX9AW5QYPSLLJGo1I7jU7a0ouVsNkVjm336NeXGF8AJRZlANCJMt5GiCqAasg2JlPNuypGotpbNoV2NNF5lEKekRStAQmC1XLFYtlSi4Optx7a6QviOrKnSZo4LDG4ewNpEhiEXhxLIOPuZluc5jB0nryVT0LLrVy3uuT6bMTmZIpcjLnGcffYU3Nu5ZAVyIA1WLwNnpAgFc+sBXX57FTtBSphhMUAhBXsQ83IU4R4osJ1jJFy8u+MdmJQD69QUvX53z9L2nPHjnKflb92ifndN89YLs6ozCttHs3Tu0MygfuYJrAucEFiLiFMIadAgEVAR7hGQgFaM8Z5BXkGW0QmKLAnH/gOzdh9STIc9eHXPx+TMGVrDnQbcdwliCDQhj407pPcZagnSxuOuiUbaxDi8VMkRJFp7Eog9kQlBqjW3bSHARkWWV2Teq3v8H1x/cPUp48J1BlWU0Rwo+mcJGWj9E1LPHPf9TuAY2JWN62CbECUms9K1WK/K9PYoffJ/us88IF+coFxeqlPHgFNYhvLkJxmVA4FG2Ri0cTE/h1VcgJCHPsbokKI1PXW8KF9tuis4gvUWrWNn1IR6eqQYfHehFdH7GebwLdJ3DNI5aQLM/Zv30KbNqgrF2E+DcfMwbQ2OIwEyf+IfQE9Qjc0iGNCDhxmPg1rk2i4ayslx/+IBv/+YrZHAcFBJnA+0gZ+Fb3nr/HuL5C/T/WhP+9Ts0ZQIlZJyMvX8L1oOwyHEGKpoGl6pksh7xk8cfMryzwyd///csZnMqGQ+RjalqlsWKiU2URx8QwqJWc8JqiVhe4bouemvoHJ/leK2ReR5BH2PwXYt2DmlM1DZGNWEE27zHhd7zJCXjMtbdfAhY52k6izEWPyqQ771Fd3gYzW37zHMDuoQ48dO4RAsRyaYrR5JqEcQmXhdCxjEFbPhPN6LbuETybHLW4Rax4h79gQPBWbz07H3wFiNfwlcL/N9/TfYv3ufBX/wJ3nUxvbNRl+6NiW11n9zDv3qOWsXNyIdAUIpyd4K5XsRnqCTSQTWV3Cl2qSbv0oyXXMyPmU8v6YwlEzIFyrG1qEgbZw/TBASOuGdIEXcBR9jIM0QQSBElP8652L41eSE40+GLAr2zRX7vELmzhdN9x5S43jY7TuiZKCmfIvpL9RKe+HYSIVJ1JwE8G6lP6PXOfgPG3uoYxndPrD+5YRj1tu6BBA4mLX5vcAY3Hhq+r8SIRKP1USbV+0IFGUGyIKIWPHiL6SyzrmNRVcxHQ7ZHJZPxgGpYUi2XSBdQQaBsIGsNwQdUb6a2WR4JOBFR+oSKXRy8UhitqHPNcjJkXpZcWc912zGvW1pjomm1vjHXI31OlVCbTCvyPCfLcm5aLyd6e/+8BNG3J4QUEcbv8UlSujF3F6n6kSp0WXG78igBbJVDqiJjfnEV56k3eAHZoGBUDskyhdYFy6bFtpGOHRrD9fUam9htXWvJch0DUx2TDRM8SM1wlNHWNetmzcvXxxFw/c4H+OFfsPPTXzCZX6KIMr8ePBPp76vxmEwKivUSKTPMs28p54v4DBOTKvjEG3Q+sQvjRtZXf3q4Jn4tyZ1XNSA4HSh+/sVnHK9m6EFBwNN0HUEZcpHjVxarA8PxFoMcmm5GGwJFCQOtmYW4z4/3JlR7FXbZ8vqbE9So4GBnxHpdk+Wao/09CnmNHI54uH94qztqCB5hoqRECUnQsb2obQOmbWJTAxXNvmtjYnEFQdPUuMTi6xnugZhY9FihS2NrlOLjr74h0xLzw/dZ3z9k/1cfM3nxkrxr4jMPIZFvImMs2Ogvg9a4QtOhyV1soxveoMwLQgJ7Jcn1KzLLSL4bPdoKG+KzEGClZLm9zdn7T/l0ssXPfvclNNdUW3uURQ7Lfi5BVycfggCmjqB+lilmiyVHB7vMrhcIJejWXWTBhiTvIMQWtv/o6lpH291um+HBoOCPf/xuZBUkACYTOiaCiS0iksFz3FfiTqu0QhISUCBvsE8hECHJdSUgdPoFUge0jhJqnSvIJD7L6HRB52K1VljFehHZ6b4qaJYrhsMJg8E9ikmO0gkQwSOCxXsXM1WhCBzigyS4gO0M1nSpWCWwOVhbo0VkM45HFYOqTKG0pCYw2Ntl7RxNcw3e0y2XuLohOIvxAbNe0zUe7h6yszVALixi0a/1aKrsrCUQcEEQkrZcKY1AYL0j+Izd7a1bZUwNqoI//cF3ooluakShlERJvUlaZWr5LNJeiYyM4yz5S/VxgEzFM4/feBEhQCpBluWR4SsDutSxo6Eu6NBRZkvAuY75xTWSgF02tIMRw9E25dYBZamQMso7ZAjgLSG42L5dajwS7+Pebtoa08WusM56ZCcY0qJFbNsegmJ/Z5fjiwWeKNWstWR4tMtq3dIRsKalWRg0sSBmgqBrLGb/EL1dUuTRPDyYWOARMnXXbE1MsBFYoVHSp0YTFm8clDAeR+nLbV6DquCPf/Q+SiYWk4ygg1QKRWSmSaUJSkMCd5VWZMQ1KARIrTYFCBkSY1oSZasqejVJIcmUjkVMGaIvSZYhsxyvcxovcE5gQmA2XWI7z7qZMpiMqfYeMjoIBFNDsHCwhwBc1+JsQKgcnWsOP9yF70lsELTOs057oXDxXHTXp8ynK3buP4ntrJVmZzzicn4Vvamcw0nJcHtMIyxL0+DalsZe45YLbBd/3mA4ZncwZlBVZO0aGxyp/Lbpn+OSP1OQ0Qw/l7E1uFJq40t1e5dASh3ZakLiXcA4SQiRzd57t/QF1r4hUPCBoEM8M6VM7BLS+pUEFWMyqSRa6Y2Jv0rKigi0KpQqyPKcvMy4nDagnnP/0X0mf/YE+eNHmONT3NUU3xn8dIW8mMXuqFphc4kuNIMixy8bwtkVat1EAElIiiynGg1Rw5I213TDkuxoj+zOHoxG1Baev7zkb376GXmY8u6dXbyqCFrgpaRLY+Kti4W7TFEOcqSJahTS50EJrFIY5QlK0vOiVRAEE9dmSMlFAMjZeN78H11/MGijlaBb14hiSC8PCYhELU2b5caitU+UenDCbwyJNzV2AbGPSZ8JxGCkbVvccMDwJ3+E++xzVl99jXKGKtOUod4kL30OI1Kg5H3At02kssblROhqZNvgBRgXE/ssbfIigJAh5UFig2bGzTbep0xIp3GBdd3SdA1GCsJ7j5jfucuCaKjXJ4V90v9GnBSvjQ9ESCzhvtLFxh1eJHOzGPSIW11+EGmizsXAQypQKkMMKnTIyKoR1nj4ziPC+yXir75B/YdP0H9+H68kQQaEUxAUAYsPnm5d40fJ/0LI1IdeoHzO4yffI7ea689/S31+gjMWoSK1OBOJnp1aLyYmeaykBI9bLDZBlCRW4pyMm4RxMVHLCOhkItYnu/E+1CZhtcHjQ/RZkQiCFzStoe4MRknU4S7qvSd0o3FKU26YGD1bJg6n3wBOoe9ClGjeIY1T3LsEvdkU3Jhd/5e4VAJtCI6sUDQSwryJHhPWokU0x2M8ZvhfPaH71bf4f/sR8i/eodvKqJsGGaBbtUgRDZeb02vKusEHFau1CRlwQaDyEik9QumYlFjL1tGYyuS0r68oheD67i5r1TK7OKVdN9DZ2KZQqtTtKjICRAISpIjBlE9V7A3CIgTBK7wxOOcw6eCXgxL18A7Z3i56PCbkCRAisWF6UoVIWtJNx7a4N/kEwIUN+AY3jknxOyF2IIjm0z3AegNS3Oa16bkX+kNZxL1ApH1TCpwWm88oZGSx9DtqbCuYwDsl8VJsWtEDKbFLn/0NYyWpBM5Z5osFnbXM2oo8z6iqkp2DLSovqZynaFry1Qo9X6Pqlsw6ZPIY6wtAVglMkcGwxI+H2NGQuiy47AyvZ3PmszlN0+FMZL/QF45cnAdKRLllMCFKjDxkpSbLCqTOI8MuxJbdntQSXvRwV2QQSamQSm8A2/7qu3JtjJ0F5PntgjadsZyenZMpaLoWUWdYa1BVjpQS2xiOr84YPXmM8IZl0yYPnhVeSfJS41Yts/NrVNjiweEuLy8voYzTV/rAKMuQrcQIjXOOb1+9ZrVa8ycf/ogn/8O/xv/Vz9k6fY3sTQlDTG5ECHRa4KqSomloqhK3qlH4CJT7fh34dJ72dIzkOUIS3xM7/sRpKDF5wfT9t/jNOOdnn/+Oa99QjIfkRcb1bMrO/ih25dAZtovgw+J0RWMNelgROolAoV0GFrzw1Ks1q6s1VZDUK4sejPFYdp7uIUpP0JpQED1VtI9A3S1dkYIe4hxCoQWYriMAWfJNkiJW5F2e0foOJRVaZXFvC9GHIYY3Mpq7hkDwsFquWM6WjCYjskzRecu8scyrgut//cfsvXrE9hfPGL0+pWjquEsFCM4yOLkgDDLaYUlTlGTTFcLaN5hjce/ok+a4fSaAUmyiLDblhxDPUCclze4OV28/5OudHf7+1Rm/+OtfsVxNef/BmMG4YntnzPn0GhE8VaXJC8V6bbA2ylCcjcy3ummp64b9/W3GhwM8grOrK9rGpyAdBlXO9bK+ofVnisneEHW7pDeyrODu0Vs3xRsCGhErsX1SIfsjxqdfEqnifPcuADZKAHUcbwnRiNR7UI6giIWLTKAz4jrKBFZnGFnhQk5IptVKSXbHBcV4D5llBAJKgWeNc4Z1rag7iXGC8Shnd2uMzpKsxScafRb3rOjHEvfAyW4J3TZBaEzt0aLie/f3Cd2CvFQYE9u1GxfXtSQl8N6gVSxS2K7j+uKcxWrFeHuPnd0xWbGK8nyZCjYJGDStpXWGoS5iJC8E1nmMs2iV3Spok+ucw52jGB+oZISq+t2+L2gEZDIxjQOU2OkJsO+fvZI3WQo66aKkIKgMqSMjWCkQmcarnI4i+og4T54rhAgcHUwYVDkBjxQKJTqCsXifsTaetQ10KHbGA3bGJaqX3zpP6udIWeTJlyTeo98ZsX9QIlRB6AJd7Xl0uM/r169RWiCEwoqAzHPcqkFLiQo++kgphZIi+vnVMy6OG+aLLSbbW1TjEcp0BBmTxSgxigCUNYaVMQyy4g1fPGiNRWhFlle3NoYQ5+yjuw9jEUIIpIx5QvRiTFBzH5/JOJ7RW6gfwxigBRE70OpAYkAncwIBIRUGs8wBDrRClRlOaZzK8EFijYsTyDtcu0TLHG+XrK8bzLqkqkbk1Zi8KqKnmUqt1BNzqd9jIRbFKmcZDk2Ud3WBYB00BWZVk/fFPgLbwyFaXsfipJR0UqGHFaZZxCKX99iuJqhEfkBQz645bwzd7hGHhzvkRYZslgRH6shLZLnYGOhGnzAfwY5YtXpTVf9/+lJKs727H0FvKZMXT7KsEDEuVSn2CmkvVVKkgkFkyCFvGHMbY/9cE2TsyphlCiFUAvai3DzLU/xXVuRlSTkoEHhMvaRUgkIYrJa47RFhkJNpSSYloU3AdyaptOJBpvBC0Z4v6b69xM/WsW17oZDDAjGpYFgiBxVeZyzbDt+2jERHpnLGecVwOGZ5fRXB1EwQZI4oc5yMDKrWdBSZZDQsyMcldmWRRYa0fVv5m/gVKWL83hPkEZG9FSLQJ0qFxP2zXm9/GGgjAlrltLOa/OEW+N7gSyTgpZ84N8lQHzL0k/73JFI+pR5RRxKrVb0kxTtM6zjVmuqD76F3tmk/+QyzXmNaRyEVpVLR/FTJGDCl5GQTuPfJgIgBlBMQvCdXMvZMR7CRvngQWkGhqTvHlQh44Rh6KEJAtYH1umXtDG53AO885WpywDq5lMeP2h8QEeV39B8/lvR/L+nrq13Bpb/ESELKmMTZELcmyS2uQIBxSTtyNH/1K/LlGjsskSFqlfEC18bn7t8+wvylwv4vP8d/DvLhTsx/nSH2NogBhD2dY1eJGpvMi0WQyKlDfr7gSXfA4/t/xvOtZ7x8+RXr5QLlAs66TVVEhIBUiSGRaOLBJZaMilVDGQsZOAmmD2YUEPymih5ErK6rLMqfplKwkALtHIV1ZNbiu5bGe/xWhXpwD+7docuyBK5sqBdAf0CE9Jn6APiN8diwpvpxTbCP+EeApfj9n3tbl5Ik9oFHbw3x4wHhfIGbLvG5w69q2q9PyA8V7q23cH/xDv5vPib8b3+P+ou3WB2NUDJj/eKETGeoqqRbXeC7NV6WMTxy4KdL+MYjpzXSZ1E+LjxGOdyDLcSiwT87I5t2TEaSh//tX3I+veD82y+pbYtZremWa0RnI21wI2lJ0iURN3Yr45qxxkGeocZDQuEoyhw1GTHcGiOHFaEoQWksxOSzB0vTM1Yh1ZrTekwziN7sO+JGb6QysgeM4z1txlYksC5lGuK2ixjQI84bIKNnNAgpCBooMvRggFLZ5rZQOu6xybC8ZwCJVVyb/VTrmSa9f9aGK9HjGiHggsV5hbES4wzLFZxfgFSKqirY2x5xdO+QgVb41sROUUTWXOxEJlmZjuu2ZbGuma9qVscXtG3HBg4LsTsJIr6uDy56zFEKmTTr0DZtbO1dDFBap25YdvPgvfOEoEDFSqtMdGrvPN7F9rtuI7kS/dYLBJQKZEqibtzOb+XywccKXN1inUdrRTUpkWWGFNBcW7wLfPnsOdtbQ9QkRypBe71kvDWmbVokglwLTGdpc08uNYUso4QwNJxfXyOCZDAsY/ypMy7mU37621+yePd93v7v/4L1rz9j68tnVOvljWGqcVSvTmE8wozGNMGj23bjNZUWDn33x96rS4jI2Oi/b9MHTGY0+/ucvvuI37gFP/v0Y1beoAsFa0OpC/bu7jAYjXCzGutaMilxi8D55RX5/SHeOzKfDKRlhioyykpRDLN49vnA5GAfPSkY74+Zr6Zkg4I7D4/Y2xuzalqmfoH/g0tO//zVy7wFAaUUZBEEzLMC6yNzzduAEpKiGMS1ICOlvz8+Nkz2EM3BF7MVi+tplC1ZQ5AitbQ3WO+58hmvJwP2/vSHHK5qtp49Z/T8FcVyFaXDBFxrme9U6GVD2bURUBAAEhmIyZAQyfMpVT98Kh7A5saCkNRasj7aY/7uY053d3jlWr588YKT63O2DxV72S7bdyZUWyPu5wVetVTjgnzouXO0w+X5HC9ajAs0a8nJN9foPEPmgq3tCSpIWt9x/9E+y+WCLJMc3B9RjgTtbw2XxzU7ByN2H43ZPtzh249f3uoYZjJwVEbDUxmp07E7Zqaix02f3AgRK/XS46WELMYTwUu8i/NdSBA6xjTexRhSKB3ZmEpjpMTJyBqwAazIsGiE0FHAqSJD5N7BDo/u71N3HmPjXmFMh/VwdrWmWV+xPDlhsKUZbL+FsRmgcD6yu6wQaB33Eulj4C+yAX4wIXiHHnqKHct/tf+XdKsr7KpjejnlarVGXXcEHRAZNF08C8oiJzhP1xmCc3TzBVfLFYuLgnJUkg1yhIRcZhiTOg85j/UmmuLK6CASSAUCEW4VtNEycDjpiLluatcey/QxxhcygTIx9hdCIHQs/LoQJdiRBVYAAtt3yYpVSgIKRB7HOJM4klF+0ISgY7FOBrQuUErz9O2nbFWKprYYIXDex/bj1nF6dkFzfcHV+RXsH7D76A5GCMhyHBqtszheWqN8YjRKBQWo8SSCYtZSWstgf8jBoy3MuqY5m3E6XXHR1mgchUiSYyIbdTAeUC+WeOtwtmU9vaCdX5OVBXKQUaS1L7WMdpwWEAHXWUSWWiAT5fXOe4TIyLLb3VC1CGxnNnl3CGQm6DUiAZ18pGLmiJYRuFEqWqu5ZLILyF6GGDzBgacHCRROR6ZOjANASI2XOZ6CQBY78RLnitI5dx8/ZjAYEKyLHRSFwDuLDx31csXl2rKoPboccudwm9Ewi0l3ChylCFG2XZRs+sgTGO0NuPtWh7eRhWhayf27u5xenlNmiUnuHF0rqBtHIDJqvYiWADrXsf28s7TtgovTmtn8jK29XXQxgNrQswKFlPS0cGsagvMM8zzuaQlkuq1rMCj58Q++E+WJMu6RgniWSBUjyo10kQRUC5KZd19sjKBbb0Mgk9+UQCAzhVA+AXspFtQaMo3TGa3TWC/wxlGv1ti2oRNQNo5iUDHaGRIYY23syKRUfNbWOPAuyu2UJn80hAd34t4uIuuqcy4ChD7grcfWLevrFa5Zs323QmWSQVmwu73N/PJV3Pdt7FIlc0UnJKu6ZTadUeSas29OuPDHSO/JypyDSUVe5dAEfBvvp2+CFPWCsXgam36A0PFeu1mDHPzThcU/cJUKtMjx12uyJ6mpXTA4PCooQMZEqU9Se6nDm8wb0vxPf0hYRwJ5kp1UuAErgrWs7Qq9t0/xkwHh5IT6+SvqxYJKOqpMo0OI8pw3fDGi431MVlwIsTuJjJNJEZGtjVkwCUwKAazHIbjOSqz3rOqWcl2juxZXZfi796nv3WGth7TGpc4yROQ6MXfCJvlLn1+88el9pJ5GlMhvckDfW56mso7zyWzt5sW3coUqx/+r7+HqKwa/aXBlEZHaLC56hcA2XTQAfuse3aNt2q9fMHi4QysdLhi0UxEIw8FQMfzgMfO6RdQmLgrjEcsa4WpkVSFPAzurIfXuY/S9mtnLl5jFChUgV4ossaKi23av049u6DKZnASi7t6mZ6ESI6FPCkM0FYjmeSaaInpVsAoK7Txd3aGtQRQZ8sF91P0jQjncSEc2z6efm0Smhe/pfn2lLfRAA/xetp1omxCR7t4EMYlPfw+svK1LpY0OAU4rxt95RPvzj9HPTxEPJ6wv54idIfmjPebLa7I7uxT//Qec/X/+PfqnH1H+mw9xSmGfvSIbF+STIY0SGGdQIkMlWYoelqh7u0g1JxzXOGci9XdtsM+v6W21RIhJosoH5K5C24Lh+w+wQsSOJl2HX84pW0PRetbLGcu2pZXx8HVCEIoSUZZkwwo1HKKUIisLRKZxIrGbesPnVEOSiORF86bxc7hZNxvGWs/0E2n+pj/7NMD+96vTUiRWSAI4bts0s79udgo2bLN4ykOwHte52NjdJ9afsDcBQw8mCgmtiRJO6+mMY0Nh8311OcHU6Wc7ovzTOxflVFLQmegVRIjU8L3dMXuHB+yNRqyup7T1irrrktdBDHa/Obnm+dk5Ho/zsVUlwaN0ZMF4F3/1bMgepIkUdBIVNgZQzrm4/0uNMR7rGmy6P0RMHqRU9JJEISArYvW3a1vapokmssGTZRrrPFJKtI6m48YYrj+9Xe2+lJKnD+8yPbtmPlviCOyPS1ShmF4vMZ0HB+WwpA2BrSrDeEuhFOvZEusDeZXRNnH8zq5mkaHTeYyJbA+bEgmhoTOOrJSYznF6cRH9Mt56myc/eoeDJ/fY/ugLRq+O0aaO1WoXcHXL/GAHeTkldzYB3XE+b0TNIURWU5qVIcTwWooY4LeTCfMn9/lmd8DPXzzn1WpKGEoyKwkqUG7leOkodwYc7t3lYvWCgKOzhmZtCblkVAjatcFJhRtUyKFia6tAe8nW9gBsjS0jiLOsZ7hrx2SnYt684nyZ422kVk9fn2H+mbaY/3njGJNUazukEBRFgVAglCJ4h3WGXGcxsQ9RshklU70EKXJ7RfAY71nOlswvI2CTlwVFWUWPlFwjZPRNa8KaK3eMK4bUdyfkB++x9YN3GL04Zfj8hMxHerzXBbnrCFlOaPsiSfpFYpbSywcDXgmkF1ip6TJJMxmxuHPA9YMjLkc5C9mi9mA9WzG44/jw/SOqfMzp2Rl5WeG85GhUsnv/EarIIBhknlHtFWxtKebLFcevFnz3xx/w6P5jvn32iuPjM8pyhJwL3v/+PSSpI0xuGe4qVK54/dWCrCyohhkXL46pxrcLoEolyHdKpFYpqQhp704yi+QxZIIk6BwhNSiN1BolwHQtJggcKvmYyejNI1Os1scoKgLKwRC7iCbDSakCowGIIqPzkaE6GI55+L0/RRYDQtfgrcG0a5ztWFxfsroa0a7GlMOMyZ33MD4aq1+eXeNsx+V1Qx7g4GCLyVaFF5rr6ZrpbEHrBSZEc1M9ukM2OoLg2H7c8NjWmPWM9fSKxfk5lyfnnF0sWTaOulEE67DeRaDDR8+oLA8IWyKBohwwlJpVVyea2U2BIHauiYwykYp1t3UJLakOhkkGJCPrRwiCjGNnvcDqAivUZgy1DHjXRAPToHExMNj4uwQVT9nItNIQVDToNdEXLVpABYo8kGeaIo9S3yALdh99j/fefwS2wbUNtuvo6oauXTN9/YLrO2PWq0O27z3i4O49vG0wXcfp6xMMguvrFXmRs7s1YTSsEAoW8yXTxRpjBdZnBKERVcFOtUXwFh41PHI1zWrK4uKa6/NzLo7PubyqWa4Mzpno/6Y1rjP4YCMrd20oi3EytZFUVYm2a9bGpkJJlJ30ADPEuEBJdcPcvqVLadi9kyF8XHdSxTXopcf1eZIsEFrjhYwAuJLIENvQWyQGgRcKKwXGudiWncjA90i8yjZAhZQC64ldvYg/z5MkOFmOUjlP3nmf+4cDumaB6Qy2szSNxTjL1dUZTXuFqE8wq4LiaEguAsYEglQY59GZxgXIVA4+StskAnSFzwMhOLKBw3tLuTvk4N5dunXNenrN2dWci3WD7RwdDhtiJyNCINcFQlpM0xFCoDMNxnS09RJVlgwmW4mtJNEiGt7HdMMjfUg5tvj9YPIWrlxJHuyUSORmvkiZOiCR4mQZEML1Jw99kyCkQKJix15c6pAagTkhVJQ/yQioyeBiU1ktcZnCqAwbFNZ5RFAIJfFO48MQqyR1F+8hNybKp7IMrSVSC1QWLRx4g0giiKyuWAQMGOejNYbzMZR2AS0z2sbReUCquAdqyc7WhBe6RKCjxFAKsq0RK+uYXs9ifLZcs17XUeHsPTqTTExFpQLj4TY6i4w53yMdqUjpvItqEBHxCq0lTiu0ui0jYsA76K4W0HWRKSNI1eLImLhJT0PUu4W+fsemek1I9N+4pPpiH8CG0hpSwhG7vXjarsMUBcXTt8j3DvDHx9jXJ7TrmryzaKnItCTXUb8MPrV5jffmfUg6TxWrif6NXumCjXGes1EyMvAG0xqk63C5xD06pD06ohmM6SAmJ+lQeePmf99Po//5if7kRUjVPH8jj9oklP1rEsPD9RDWLSeKQuCrkuonH1C9fkXeekZ7u9jtEeNmQfvbGTrPYrCjJLx9B/vNN1B3eCnx3uGxxCVqCUtPd7WKQZHs8M4SvMEejvA/eky9brHPfkN3dQmrNe/83/9brp6ecPr1F6xev2ZxNSMDMhXpcUp4cqWQiZ77plutTYlz1DLHuRg2RrIeYjMUrE0Ubt9R2dg5gmGBf3yf7O4RoRpitOofdwJi4pv4N9kWyedEpH8PfcKf5kysbPqN+imEHhSQEdBMaS2pOnTrl7yR+3gN+Tv3aL7+ltVHX1JlT8jLAjGoaGTA5FDXK7xruHpvm8GXJ/h/90vywQS9qNn5bz7EZzI+wxCQQSY/nhj4dIn94mUbjcVEwFlDe3qJ1zKaq/qAs9GHxtZN9J0KIIsMVRZIqRh++C7/4//8P/Hew7fpZie8+uxTfv3rz/ns+SmXl+ccn54Rsoz79w/IVJTaCdJcSCBrcgSNfjMiJZtpnWykQUHe7B+QTMBTa75wI2kTAWTyrQm9R1H/W4z2kKntmd8kSbd3hSRU7lt5O0KiVMeDRrpodkYQ0aCwrxD3c8vHeQcSYS2mblkt18ybmLCHEFJlRvB7G20QCBylEoRcJ5DGs1zWNG1DIDAYlBy1HVJrhpMxZVHgnaEzlq7raFvDctXSdZblYomX8RCKdF1J5hVdZwkuIEUykw8S621aQ9EAFCLIalOlqcg1V1cLfFjGOeWTnI10/wmwCSFA8IgEKniXqLHeI4WkLDKUEpSFRlc5wQvWteH8anm7gwi8PjlBhGhynBUabw3NPJoMS6Ew3pCXOftPd7HOIOdQ5CWnV9esO8NwUBIAg0dqgfCBpm3w3uFkdImzxhGcj8yi1F68LHJ81/HpF1/w5bNveP/pW7zzl99n9+ptqk++pDw5RSqJ1ZrWBoY+Fkck4iZoFyoB15FN0q8LACc07WTC8uEhp/f3+BbD7756xslshfeOItcMJqPUKchh8sB77z5m8fo5nZ5DNUA7jVKWKs+Y7O+BmtHWoLdHHIgtvvn6K7YOd3lw9z6L9Rw1HCFDxm7Xsbg4xdZX5FrQho7J9iHXJ89R43GvCryVywdPU6/JUvCX5xXBpeqYVJHm7AVIiRAR4IwdTCLjTEgV6zYhEJxjNV0xvbpGBsjLnN39EfcfHWJsx6N3tlGuQw8CWTmmdiUuu4atwOvjJd8uFIeP96ie3EHWLVlrqDqL2RnTzBfo1SqagxsXj724deDSOJsypylyukFFuztmNRmyLDNeL2c0fs5OXmKo0bajqjJkmGCcYXo1xbYOQYsaaHCSTOe0XYv0nnbVUpsOIUrKYoDWNbYL/PZ3nzMYZezeK3n05B7OWLrW0q5b1ouWIh9g3JrDhzu88933EUEyP79gulsyW9Z8envDiJeKdbGD0oo8TyW8EL0Ig8pAZFgnMC7GYkroxKjIcB7aUOP8TYKRkBq8TZL1VKwReIKz8Rz0PvkwRJ+UkBVIKWjna4JryaoS8gnoEVJHj60sZVfju54QDLiO4A2EaNBpO8P2zoz1ck5RXaF8w50nj9i59zaonHtdS7e8pl7MuDg9YzVfsmwNq87hgkboAbIIZMN7DPYte291PGyXtKsF68U11+eXvPj6Jaevr5gtGlrjsN7H/QZPZy2nF1eMdMHBwQEIqJPcOfqdRUA3EsVv91AMUmMHh/FcS/fjZY5XOd55OusIKenLdYWQUUrUWRkZNSGyYWIlRxAcWBfPnGBBiMiO987Fsk+I54XWGUJrstGIerZkvaqpBiXFcAsvJ6hyi6yETECV9svDd35I8IZg23T2KZwP2LZhsnOH9XrJsDwnCM2Dt9/n8MF9hBK4ZsH6+ox6NuXs9Ir5qqY2lsZ4rCwgHyIlDCcPGB0ZjpzBmxV2PmVxccF0NuP1yxNev7pivnDUncWFWOgMPrL8rPGc1WvG5YTD3VjgqpdrnDUEmWwF6L23YjHltsfRj3fp5WnOBYLKN+9trMDJAqRGqgytFV54nGvjWeclLsTknOhwkGI9n8pPCull8vCLRW/rXWJ8KMpKMhgN8KsI5jmh2Dp4yv137qMweNfhbPSKsqalnp2zuLpgvZjhydm5/widKZztWC9mXF1eIaTi5PUZ1e4dtvf3GA1yZHBcX15GQ30vcF5FKXGZMbozAu/Zth2HJgJ9q+trZmevuT494+pswcV0TRPMhinjnUtUaB8TeiVTzhxZWsO8QgeDXTXx2Yrk2fSPlAK3cWkZ2B328bNMLLRNdBqlpVKCSqytEPC2Z2OTfMSit1nwscgcjYklQWqC1GRa3hRIhSSIHEQez9tgo9WJkuwe7iOLbYIskd6jhSXqxjzgqE3L6nxGM5szyBW7+7tUkzEiUxsVhhSx8JUHhS+yiAUEAV4wmnjGOyO6ro2sOBNQvmO8tcVgvAdKYZPPoCwUwTcEpQmeyPQhsuAkAts5ppfLyL7eXnJwcIDaLhG1SBgABC9wNraAJ0CwAdMaNKTW5f/EmPwhAxgIdLalXs1Qto0BTYgLPjIKBDccGbExRYx5VoImQp/KRsmCEDdAzc373OA7XsQkSwRHsIEWi6ly5NtPyB7ex1xeYc4uEFdXiHVLJm00sazy6L4dIiW+f1JWBoSKVZQgfPJ5EHil8M5h29iqUuYZclxh9/YxW1t01Qgj1MY4IxCrkiEJDW+S/35QbkyEY64f3vx0cYKmDxmZHAnAETEwdD5snuetJ4rB04jAeqtkuAj4//gZ1b/5I8o//iOM8KynM7ZEkpxNBhjh8cagHx3glzO87SstATNbYE47Qr1OwE2C6BYd5tkl0rvExnJoL1DlBG0X5OU27o8PcbM55viE1fUU0RhE59AYcq2ilEJAcC6ZWcXnJ4XAp24dLiTUnoA3AfICWzcx6Co02dYIcbiL291GVoNYrUmIdC9b6gkJN+PyxiRMc5keoEl/TXZHcR4EGTfNzWAmUI5+XGNF6tYvmWrkIiYQptLkP36b+j/+msXffYJ+9wAxGbHyDmsMRZGRa8ne4yPysqL99x9jTxYM/+hd9JOHuKZG5TqSV6VComLHMOOjgV4I5FqDF7S2ZV4Y8j95lzzPmP/vv6C77rBIbHA062XstOLjmk9ONnilENUW1d5DhlvbBDXgrBsy5QtUnrFa1yxNS3KbS6/b9LVA9pJMeulaz76BDQWqH7sQgeReIhXnPpuV2fvY3IA6mymwAQN7H5QbZO72hi9h7r9P2OohX3Gzp2w29eTAL4TffG+/nUSHeI+zDtMZ2qaJFOMNQ6ify/3BLtBKkFdZ8jWJB5ozhrpODA2ZPMC0ohwOEdWA4EOU8LQdqq7p3JzYAK7DBhcDV0ALidFJTx4ihZY8i4FhAGstiOg7EEKg6wxt2yFEINeSerWOiUTyr0qLjd5wdTMMvXRNpjWd5HJSSLyPAFCZy8jDC/FQbNa3162mv66W83hY4xiXA5aLOat1h8gUk3GB9gI5ypBSUNQZ0lpmtkPnGbkJrJcrMpWjlSbfGhKMiT4cSDyagKQTHdZFYLKrW5pFy8rXzLWiLHNMZzg5veKbR/d47+kTDv7sA7bW78PVlNB0DK6mlOsO5+IJ3E8LQWQ6uaBwSmGkQpQD6q0xy7u7nG3nnPoFJ90J+agg31EM0cgqRxeaO/cfUUioRoK1WVFPzxkMPNwZMskecHl8SaeWZKVi/96ALK9wXqBDzvK6Yf9oF+FWzOtjhtsjxlsDQhs7Mx13Aqdy6g6E9TTLKePxGJEbhLC3Nn5xP0h+C0rFgk9qHRziARPbzvuA0AqZKvMiSTUE4FzAe8diumB6eRXByyxjMMn5/p8ecHRnnxdfXdCZGfia0XjM0s1Ym1dM8j2WqxYpAp1ds3A5C+eZzebsHWxRDQZc3rvHxI/wXQfWIKxL8qhYPfdS0ilPQ2DZNtSmY+lWZEqQZw6/ZTHTmsUivvbk1RXj0QjTuWjKSMW6abm+WlBMckTbEnwgG1uy0nLn8JCmlZyfXbEKHauZwTUzdO6R5ZDDnQkiWLJMIrOC9XpFOZLs7I3xYcS6scynS7Z2SgY7it2DB1zNZsBntzaOAYkrhqgsw0tFwOGcwThPcLEte/CB4B3OebQOKB09fgLReDS4Fu/MxpfIhyiPsu7GNFkKn7qqRdPY6H0WGR7LZQcC2q4j2JagFSYItO9liEm6lQqKkW1w43eoCBTeM9zaZ8u27N2ZYtoF5XALsm1Ao6tAPthndBjYe1zjzQJTL1leX3D56pizyxVLE0FXpAZdIPSIcnhIte/Yfdxx7zvXzM9OOX99zPmrU05PLmnTXhoIrJZzptczyuGYvbv32d3ZJ8fQLtabooEQmtsOUIOQ+GyCDzaykpzH+8iOicB8lJpILcl0LHj4EJBkOEJkAodYNPY2yWZtMvxMzzwJpQmp/BikRAZNbRztfI01hrppKKqbde9JzOYN60CBSGy8nJ7rkM5py3BnH9ut2N0/xHSWavcIlU8IQpCNt9ge32M7WI7eXdCt57T1jOXVJeevzrm4XrPuwEuNVxlkILMJeXXE7sFbbLuOO9+Z8vT0FWevX3F6csnV+ZzluovmpyHuRcuLK2atY7S9w8HhIft7Ob5rWLUujZrA+VRgv9VRTKBNtYf0Bu9iDuD0AI+KxV9hcTYySHOtY1EYGeVnif3gPImBH5Nua2PhIsZJXUSre9PzQLRmkBqZxz3IqwLra9brBcVwRDEaEYjMNKFKslxsmvRO9p9w9LYjBEfwNuY/3mG6lm53xc7elMVshnOC0c4DHr7zPjvbI4QM2HbOenbO8uqcs+NzrucNaxswQSE06HxAhqAEJvuBu+/8gGBWmOWM9fyKy4szLs7O+earY66uDN5GFnMv9fPE+6rrmvXlnMn2FodH27iuZbWs8TZKsG87V5RaMdquCEIRhEoeoSB1TjQkE1GiqCIGoLzbrDMRosw+Am9+w1gnYiQ4pfFENqKUcb56JwhBpoJMYkvqaJ9wdHeX/cfvYPWY9dpjGoNrDMEY2nXH+nJByzZTe8XSN5RqgpAjfBMLSlJYMh2Qumd8a1xQCCJzuxCectARXBlBXR+oQqA8nDA+qOimc9rpjG5VR5ZW25HJHC1zkAYE6FKjpaReNXjnqdeW42bG1eWSajSMH3+UE1vSR8Y5KR9pXSC0HaOQCrL/xPUHgTY+BNZuSaNbtqo8GYtCdGuN1V5CbMm6MZh6s0KakqTkvxNNScOG6MXvJc3p+6M/hYTUjWeD5vmAVRnyzl3U4SFl16HnM9rXr1lfXJN1HcI6VNKib+SHUuJ1TNyDlJiuiwt+PCBkimI4JLtzSF0NabIcoySWuJn0qY/vESV6HTk9MrMBpTZfSp/1Bonqk483kq5eg/5mAhf8G6nm7V0B8N4jM8HhT77DUA+Qv7tE/MfPEX/5HfZ/8mO8TwecsyDiJuqDQ989xH+xjPSLfsuvMop3H9N9dYKol4gsgi1cLVHzC6wzaBeBNxUE0odIXW07RD5AHu1T3TmgqGv8fIm5uMIvFqxWNcI4FDJSQd0NkuxIC1mpSHdL/x8GJdnBPsFbyq0hejwilFXUyyZDzQ2A1kuv0oR8UyIlUmLve+SlH4Xw5nPsfW5uEkoQePnm39OLwn8JMxQgrbOYkqY/3zmg/JP3ufz3P8X+/JzqyT3UW48YjLdRS0dzfok8vcC+vAQhGfzkXfIffYdOF2htocgTdTVqxfEgMo1tDHbRkAlJFzqW6ylz11C+OCMbFNSrBT60BFlE6VGzRnuPSWyPN+e595G6SNL/Z1mJzHNUUZKVRaw6pvVy4xEVNiyavhtEEJGZIkJIgCc35t4ibLSiffVhg8MlIKdvz34DzvXAzhs71gZP7sGU2x7DwMYqcTMHw2YfgGgG6X2gc5ZMxioOfVU/Ph16X7NASG2/Xao6pp/5Jmazmc+K4GPnhp4XJiB2DAkhAishoLSmqKrIbPIgpSWIFhckOu8QQeA6S2dN6twETgpkFxmOcf+NFRqtIhPS+H9k/pzeT0kQPuBtbM3ZWXdzv/z+RyH5iYi+k4hMvlg+AqzWWmLX8hD/zUVvAx/crY6g0ILx4ZDF9YqduyMaloixwy4sk70hrusIEnb3Knbu5ExfXbNcLKhtbNtaDAZcLT2qyhAiYJYr2taQl5Iyy3A2MMhzvDEY51JbWkEdWpxLLc59QGYKlOd1fcX1F3N2xvf413/2r2BvH9/WOOew0wXZfIHuOmRnECF6tKAkNs8wg4JloVjliotmybevXnN9tmLyqGT3/gGDwTZH9/c4e/UlXSYYDbbYGk4Y5iWz6SXjvXtcn77gajFFDTL0qOXBO0f4QU239rz69FuOHt+nymJL4v23tzAzz6m17AyGNLMG38zJVJIjlRnl1phDK7hqLqhGMB4eYTpzq8aZLgRmyzViLMl1jvfR3BEhMN7fSBZDZF8qqePaEhE0AQjesZwvYkWWtG7KnL3DEbbLePb5gkePD8gyOH19hrWCq+vXjEZDFueBy+lLvvODR8wvWoR0+OAZ7ZZ05Qp0Qzbe5cmDD5hfzFjOrlmvlqzbBucdKpMMJxOc8hxfP2deXzM/XyBEYDgcMD6oyPMRdrlk6mu6pqW1Dc3C4DuHKqpk5gq2BqxjWGq88mzv7qBzw6pZ0y4UvtYYbzl6sE2Ri2gWb+D45QzFgr2DMeVAMtkVBJPhXYbvHKXU5FsK16159eoUXWTsHY5ubQwhgfmywJHhXDxrnBMY0xK8w/sOSTQu9z5gXdrj35SjC4X3BmstLhBjTyKI7X3sZuhSc4N4NkW/ER8ElhZpHQiJ8TExntZwvegYlpo8j0F9kAns62PozZWSZ5EkBHlBmY8pNuePhI00NcbF6DEqm6CGUO6/y+HbNe+sLpievubl8wjgNF00JhaJFSZkRrUzoNq6y+HT79GtZkynp1yenXB9vWa2sli1orEd6+sLmuWM9f1H3L13j8leTpjDbLkmBLcpTt7WJYSkC2ojqzWdiewnbxD4zTgE4WLb4L4QLEVqahDZONGYOJ0wIlavrQ04Z0m5Uiz9iEBwkcFhfIPUXfTwcQ7rAvPGMmoMpVYoLZMpcrJD3oTs8X2iXV4cOyEL8mxIPtgnEBmhMfRMDGChQGhENaAcHFIS2HlgefDBknZ+yuz4NS++fc3xxYq1V5DlUWIkFUIVDPbGVLv3OHr3Q95ZTVlcnXHx6pjLixnLTtAah5KBplkyPa1ZX8/ZffSAvaM9RsMVYb6m7VyadT7aWtzuQGK8Ai+wFqyTieFk03smBkZikcT+ATICYVJgXY3xLoHmARd8Suwjo71PayWxHXgfD4XgkN6zWjfItsMZg+uilYNUEpv2dhUEPeK+iUNSE5M+ApRAVnmq4NjavcNuM+Pg8ArjK4ajMUIXIAR6OGR7dIfde47770xp1zPqxZTL02POz66Yrj1dgCAlQsa4V6iSvNwh23vM6FHH/XrOg/df8vrl15y8OOX8fMm6sQQZlQcheIxpmV+ecX11wWj3kLt39tnZzRAW1q7j9+pzt3AFKTH5hIBC5BoXPK1xsWAsVPRq0pH1JmzAWkPXK268R7ib+NYHi8Al0rcCJxOYY+Mt+8QMF5EBnpclTsVOv95bylxw/2DIcOcQJ/Pof+kCXWtZ15arRct01rCerhkIy9FewWCQY4JnsVwzvbjEtmtW6yWua9je3yardgnZCNMJmnlNqJeIUAMtAYMShq1BxuTpI0LwdF3Lum64nC45Pb3m5PgM3yqMWmPb2ERAZRKZeaTwtHUHeBrrWLSGYlxQ+CEQyIqMXIFaKnzwuNT8KIR/vhj1B4I2DuvXyEKTTyZEowCb9Fg90yYdQqHvpNDXhPtlkLxKwubMYyMjSXT/3owzoqvJfyL0HJ5enhLdva0PWKDNC8ThHaqHj5gU8DCzFFdz1tdzltMZy9WaVWsxHkKe0RmPlRJZ7qKHAzjcJ+iSBYLOWbyIlOk+UxP9Rvt7M9q/YWx3U+2/ufs+Z7xJ+GIa6t9IkUg5WjLqDAEtI2DgklfIrV4hmoLK4JEri88D+r/7CeGX3yL+3W8JP3nM6skE6VcUmca1LdJFGVv99TF6ucTLatPO1NeW9qNvEbUHL1MlIyAKRfb9h7i6xf3ieQx8XECYFoKJ7QZDRJOllGTDEX44RN87QlhL6EwEdrqGg6Fim0CzWDFbLJmuLUskQWW0NtBlGYPtCYPREJnHksebniUCEP4NBlQCEfr5+ftH1Ya/Qc/c6DG3np/hRZTsiTSr+7arkADFOA3+0d55y9UoSMZWiUlhHVhH0Ar9+B67//qPuPjrn9N+/ozJtwtk/iIaKbZLpLKwUzH8r39M9vZTXFbGjTPLcGWOwROEJwiNDQ5TN7TPp+AEptqmcyumzTld28HvHEtr6OZzMqIxuM4zpHVoF72kNhWt0G/ePUCpkFqR55pMaSQqBlcE8JEWiUgqpWR9GAIbZhXpufcG5PHymyAOEiMKNky/G1pVdPDt54B4E4TrgdUQ3zykylXP4rjV603T6v5e0/vdfKpA6wzXTc24LCkTOOH7uZaon20yYZQJzPChlxCln5t+6w3Y4/O5eY5CSjKto0dBiOpbKaMePMur6N3gAj6X6BDQxqJlHqvHKQDux8i7kPyi4rN3QmCdQ3tPlucoHX1CpI4t372P+mbS+lSIpLp9Y63+3nOLWuiizMhyhdZRx+y9x1mPtwFnAtZZTPCYEJMyk6QPt3kVVcaf/Nff5avPv6ALHYPBFjuTMfPDa/KtIVJ6ZmfXhMqx/2DA9OqcFQ35eEAlFacnS3bubZNXgaIsmJ6uqVTOclnT6QBGkMkOnMUZQ9d25FVBWeTUrtk8m7IqGewOefj0iMXFGWpnwHd//D3cdMHs6orlfMp8VGDbEaaNiQlKoUrFejWj8S2L6QXrec10uSYoybpzIGFx7Tg6BDu/Qo5HbE8mvDi7gDCjnjbsjfZQBETdMgwFazfG1p7FsGbgBE+fvsdWpvjNb37DKB8TQkvbrnGzOV1zhfEWqg4ZHL5tcCVcv1rQrlvOL8/Z2x0iyujXIO0a27W3uhaFEBSJCRaTrrhVBBfQWY51DRD9n1Byw5Rw3ifQRrBa18yuroFAXsVWpUoJtnZzBILJdk4x0rRtR+0d337yMZNRiXVDRNEitOfi4pLh7pDWdRjryHLB7HzKZLLDVlVRDEZs7UhGwxLrHaazdE2NtQaZF4ShZ52XSDlhcbHEeUfbdDQvWpr6FK1zylKitWS5Clyv5iipyLtANcnYvzfhkC2m5zMQgWpXU+5KMr/Fuq7Jx4HdIufq+pK2g6YRdPMWKTVHD/cwzJktr6nrEoWKXdR0YDwY03YteaUYDycMpxOcsNGU9RavEGC5bNHaxq50wcak0NgEYjvIIlvUOodC0VmD9H5T3HMuyWiFQIZo1NrX9iOEnM6YWAWM+7D3GAu+dYTQxgqxjLHhq9cXNPILxlu7jLe22B4PGQ0KylyRJ7mUJBYeNoWEDXjTR8NsYo2Y9KQYJAWWoZfYCnBigBo/ZH90n91HK9bTE+bnrzk7v+J6vqI2Htu3MBESoUqKrYKD8R67d57SrhfMplOOXxxznMPVxRV1Y2jm17xoW6qdLSbjAQcTjXFt8qy4vcs5z3rdRAN6byNLxsWv60wmMMAhvaCzFu9V8q9JCV464QTRuDfGav1JmnoQ+psAzXsXGfOmjnuKlLHwGARNs+Kzj59xftmys7PN9taA0TCnyFWU8asb1k5/Xvd1nnjeJmDtjRyo/44+3gh9R00hCGSgt8n3tjnafYeDd2Ysz59z9uIl55dLrucr2iCi/1/y0dLZkPH2gOHkkIN779Au58yvr7m4uOSFMJx3dfRtsXNW01Nsu2S0s8VwvIXWS5q2Q6r/Ap59AbrWRaDF+mhbEGzs9igkOp3XXnisC6gN27n3k4zj5UzMhPouv0rJqIoxluDj110a1+R1gTeRTahkYmc5j7We6bxmWOVURUahVcxDJSmG6dfZjYF7rKWnMZSaLIvgTLxkAidC9LoK4IVGVIcMqkOG+4H9J4a3m2sWF6+5ODvh5OSS64WhdURJrVIIFTvBqmzAvdEhR4++Q72acX1+yvHxSy7PLuhMwAqBSYUD0yyZntQ0yymH9x+yvzNmXIBfN7e6FgOSWmaxjb2PDFTjYwFMKsizCN74EFk4Bhl3ycTe9iHumNHm0CV5WxxXRPeGl3NAhtiQRhcKmQmKXNN2lnbVUGgfGdNKIxxoH9ACCiEZVTm7Vc79/VGcNwG8c7Gjs3VY4xkODcPRNk3dsJov8N2Se/e2Obh/Fz3conWCq1nL9HLFcrqini2x7RrpO4RvwLUIYSnKivHYcXi4x3vvPcY5z+XVnNPTK45fnTI7v8TUa7pwhW1qEEvwJjbKCKBD8mL0gkzAcFiw7ipMq9AyygOlEvh/pq74B4E2IkTTJS0UMsvJyGhDDcjkz9IfN/4mye13Mfp/joOz2cTShtUPZU9s8OkgCuLNTVckgCO8cU9vJNk+UDc146N9/vz/+X/j0dFbhHXH8uIlZ6++5Pm3r/jm+RmX0xXHl1fUzjIYDmm9iFKC5EC9AZtSIhL/J37vLjcJVngjBbpBZvoZn7pXbT4ZcEOq2SSGmwzTbwAA56KZU5+03NrlPMI7aFrct+eEvMa9+w7tn76NVJ8g//oj8uYxzft7tKtAeTIl8wJRZqACxrYRWY02cQjnkXny4VlaXDIBDt7jWzDGoEXkKQXrsOs2GqMaH82q1M2HkyEtOJVBlSHLAcWg5F/8P/6v/PiDPyZ0Kxbn3/DZx1/w289ec3xyytnJK85PLlBlRjUs3wBkbg7p/hG+yVtKZ8MNWp/Oi162F0RI7aNvvEwiuh/p1ZJ+XNNc970RmADhYxXMszGAlf802+0/7wqxhSMQmRXt/5+9P4vVLMvy+7Df2nufc77pzjFm5JxZVV3VI9kkJbcIoSnbImRYoiwYAgybgh/kB8F68INhA7JhWU1K8gQBNgxJL7Il2JZg2YbagCRYlkiLzaHJbjZZZFd3TZmVU0TGdOdvPmfvvfyw9znfdyMiuyurbrKyos8fiLj3fuM5e1zrv9f6r4boPcal/E17uM/tX/0TxLM5ZqaUDNAYqW2DubEDh7uwv48WVRaOVRSLOkM0MQvHFSm8eGAY/cwbmCDUH54xu7hk5ucMDnc5/Id+jov795l/e4YNuZyfMd04EG1Ps1LlrzZCEkDEYV2ZSyzm0+p2081rQOJJMkHWnaS0RJp06U7t/NNsDKXJtTGhNhRpJkm6VYp8Zhbz4+0cN8kgYMOmSNxeea4Jucpde11pzcy6TSSV/gaYxYaZ1gxMicXT+MBKGxqTwrsXsyXNMlLrGlcIo2FBE5KmjJhWpi63lCGJq/pcwSlXADEWiio5rileMpVzLGyJNUU+tAxgHWJCzkMvGA5Ljg7HXM5hVSc9KRScSw5N8HmbjpEQGoJahqMBIXqG4xJXOsq6wMcmnUwYiykUG1Klp7ZMb/Axp/YkI8taRzWs2D0YoCaCSYZnDEq9rKlngaYJ1FozrUG9UjfNC7vhx4FqZDE9ZziqMBhu3S0ZOM/ewR51EykHY0alcHJ5xsMPHzI+2ucr+wdMJpbFyTmj20OEyHK+Yv/2iBt3xjx9+IjVhylSwJqIlAYrBfGywZaW3ds3GJbKww8fs5zX2EHFwd0jioEyGgjFzRGvfeM1CmupRiMKYDIcsFzOWC5XrFZr1usaNZGlXHB+cY7YmhlT1qZGhgV7kx2ObhhMVI6fnjOdrbk53uXRx8dUhbA7OGBYHVKvTjk9PsUWjt1mzXLtKccF6tfcun2LW8N9Pv3wPuvCcuPmIUe7E6aXnprI2aNLvBpW3rOaPEEry3puuXNwj7u3A+cPzymKfV575xaX50+Q2uAmlns7t651X7Ri2B3v4nOqBDGddqXsaSVHf2cj2+Tot1y5UGE2m3NxfApANawYjUeMdxI59ujjBmLDz/2xPc7OV9T1lMnOIctpxEXHZLxHXZxSjJSL6YxFM2NkhwzLMcv1glEx5KPvP+Bw55CiKHE7DmGCkhzZerlmvVoSNXDenNOslU8/eoxKQEU4u5wzHhaslmuCrBne3KG+9MwXK+aXawajkiYGxrdHLMIx88uAUcvh/hF7NyrMWlPVozUs5g0mOobjCTjD5fkSjZ7Foub46Tm7BxUnT2a8+toh4x2LtY7olagN61lNUVXM6wt2b1YYWzFdXK++VNTIajXDGaFwRV5aU3SFb0K21dJqn0TNI0GS8GN3wh5idgYdGgK+SdXookSCemxMRQDS9yULoAkhkdCkVOkQDdaVxCA8OTljEb6PLUfYYsJwtMvO/hG7B/scHeyyv1sxLi2FBSekVE5p96+EbUesMzM7Mj4RSDE/0RKOIJhih8mtHXZuvc2d9Zz1/Jjzk6ecPXzIw5NLLleKMalktxqLKScMyhHV3g2Obt3jrbfe4NGDj7j/wQecPpkyOz9lenbCYn+fajBi/2ifneEgVVC7rj6MgdVyQU5kT7IF3pOqlxdoSEUpYvCg666flRSZo13EBkQf8CEJLceQ7Ii0xW4ONNrP7yJsjWCKAisli9mCx/ff5+L8mI+rMcPxPpPdffYP9jg42mdnp2IyLBjY1G9tpIPp+qj1Bzb+UXv81xI3ba+2KeTElsCxSHXEzmuH7Lz6M7y1vmT6+AEXx0948vgpZxcrpo2gNkeHiMOWY4b7Q4Z7Nzi4e487927x9P4nPPzoEx59esx6OadZrLg8OWW0f8h4OGR3f4eygvXqetOGQwjMF7NMSMaU9hSUEA2uTMVYfEyCyqZpUhJwPjAKwaeIfFIJ7qiRJqyTHacbHR4lfW5byNXnLABtaqLmEu0mFc0Jqynf/+63eXJ8yMHeHvv7++zvjBkOHJVTrDEd8Zl7LNnF3elZe7C3fZCuGzNTSKRwmw0gAAUyuM3uazfZfeVneH1xyvnjBzx+cJ/jsxmX8zVNTsk1RtNBZrXDpBox3r/J7TffZjE94/L8nNOTC548ekI4P2W6rokhsJ5PuXz6mOV8xnh/j8lwF2ev76A/AmuvqNZd1HTShE1K3zG4rNKQB3JbyTN6JPuzgTQn1Uc8KeW0rXTYHnhoW+RFEiG3XnrC+pI6CI2vKSwpMCSnrqXpbbJ9noltTZkZLkuMUBqoUhH2SVT2DneYrxoup/vQrNkdO6pigItQGWHvaEi4MWRZH3E5q5nN1izmaxaXK+bTKfViCn6BZQVS4wg4F7lz64BbN/f5+tffZL5Y8fT4gvsfPubJg8csT49ZX15SL2cErUEcRgUJcP50ymz9iL3DHSaHExpTcu6nNGv/BzIzn1OIOA1XqyblDqpFoqbSlFnbpVuWBMi6J9qSMJBf06Y9aO6olojJuYtCdyp/xS7Lp8+6vf5lGMl0So6CUFtS7txBdgrKw7u4G3eJh5+yHH1MuP+AafMe67PH1PUapGj5oPY20z3kKJ/kvZurNHq+x82ZvmwmOHnPJYdPypbQbaYTumiD/C/ddxrMVix18JhIqoB1nVjWmNrTzJawV6H3bjNbXKI39ih/5aucLmeUv/W7FP4dyrfusP69Dyj3Jti9MaZMYdElMYm3Sj51PDzArBvi2Xm+nZSbV/+d96A0hLVPp/5R8IuUtiZ1wKZtFlWTatfn9jUbkRmigHdjyr07SVVehL0LYXJq2YuOpvFMZ8stsq91yjdu8La4cIRs0cRN32VSJ3dRStvLLm4ianLKRx7TSKQTQG7708SsvRRbH5i2okeU62ZsEmKeX0RPXK2IEczAZWFEkPEIt7dPsXtEMTkiNjWynNIspqyWC6pU6zKJt2okWlBr0EIw1mFN20NQjkeE+ZqWlLRiMEFwWhCWSXiWSCqpaQRpNEWyxFSJw0TBxkSsxlytSY1J4b6ksG1jLYVz+d7y4k4630w9mTynKO3jkJV88yySVGlCYyJYtshWNM25zUacPyEvJu26E0gRdYnzact9a9avatet60IqQ22tIjaFd680MsBiS4cfWFaxYXp5ycPTU1QaColcGGhcZBVrlssVokoZDVIqd7464hU7Jmrkcrrikw9OURVu3djhnbfucePGHidn58zrJWePTlhO1zjSiU+IMIqOwxsj7tzeBVEOj4aUQ4cpHMGn8WIkieEaSbnMu0cjvrL7CienpyzmK+omVQJ45bU9Li6WXJ42TC9qYgNlYSlLGJYl3gvNvGa9aFCjlIOSorTYwmKCwQ0MtlTG+45iYHl6f8bFyZoQlMG4ZHIwopxUuNGQ5eWMsG5yeV/D5GiEu2nw64D6iI1JVyo6y1LgOs1T5yx2OObmrkPKgCwazo5PmAx3mTfCnb0ddm/sEjRyeXZGY5b87M++g8YFNEPe/YV7zC/OWCxWzGdLhuOKnaPbvPHuiOBGjArFrxdY8Xz8yWNcucNbX3uLTz96wHD/FkU14s4rtxiNPfUqcP+9x5xO1/z8zQOstRjjiGVJaQQ3qBjvp/XO1w2r+Yz3v/8ppThm5wtkVbI72mX/zg67ByMePX1E0yiHr+wzGQyZX86wEVYL5eDGLneHYz48OWFVJy2Oi9MFCwOhDNih8snHP+C0qljXgdHuK/i64PTkjDCbU69rfCMM94aMR0P8xRJKj6kaVidT5idLqqLi1nDI/OExq0WN93B+uqJePUiVxq4RwftUqc5VrJfzrBGlaAhJYw6QmNaOToARZbVaMzu/RIxQlhWDUcVkMqEoS6qqonTK0W3DbPWQ+z84R6JQryIXFzPwEUqhDoGwdtiR5fLynGmYcWN/zcgN2dt/ncfFEjRFvaUy1GlN800qH4st8X5NKQ2nJwtWesmNOweMRzvc/+CEncMROEe9ntKEhvUyGbiCUK88w72C4bikWTf4uGa846jjkpPLOQfDA0LTMF08Yj5rmB9bqlHB0c1DLp8ucAPHKqwZhoLZecHqEkbFDrPTOUVpOT09Y365QBCKN/aILlAzp2SANTvX2ocPHz4+/lf+1X/jo2v90B4/DN64rg96+OjJ8f/iL/7v+j78yeDa+lFjxIcGazRV3/O5UAUu+V1k514CPgqFL7LIbXpvjC2pZWhCkwsV5FPwmAg9bLKxkwBzqrrWrtXJt/QEU+BsgV9MOX7wAy4vHvGoGlCUE0aTQ3b399m/sc+N/R32BiWlS3p/XQSZKpirtt9WLMLmjL4jblrdhfSqNu1SzAC38wo3J3e48frXqVcXnD/+hLOnj3n0+IzpKhJwOYUqReK4aoedcsx4/w43X1nyymvHvHbrgAcffsjDT4+5nDasppcsLi85Pz5mfLDPdW6LGpV1vUkptzkqKebqUTGLQHeesORAihwZFzOhnEPoc+Sioj6l7quJnQeMAjHpGEUbaWydouhCQKPQrBbUiwucLbHFAGyRNLXE0FawveKgZ59cJJHhrjAMioqDnSpHgG90yUQjBMGJsOOEyeEADgcEhWUduFg0XJwvuTibMzu7oL68BD/D0IBrsOJTEZ1dx+7OiLv3bjK9fJfT43NOHp9x/PCYs5MnRF2ALTFqmdeR2fEl5ycnHB7ucHDnNsOjCfV6xLL+7MPFz13yGyysFV3VGB9Qp7nRNkGcmzSh9gw8OVubYb9h5Vs1hat5sXL1JKE9OSenKrB1ip6fS9/STqWYBTFb2rsAO0CKEXY4xg3HjHf2mM8vqGPsyj53gan59KxNiepOL1CibCZji5QO00bfbF1nt3a0DO1WM7af0bJQ3ftIZc98IErIEQjX5yjqYkV8/yFuYtG9MVjLIi6RpWUZAo/vluyzy+h3vs3w+49xZ5cM/tFvoIMKBgWKYjSVOY2knEU5nRNEsSaJ0kVVVlrDN25jj0bMfvM7NJdZqLSu07/G5/bqYqi2CLDctSblsdbrOreRBTEYV1BVJc6VlOWQqijxWxVm2mm7SZPRDXuenfBWMUiyWG17Ea1OgWbqvk1lU1Ko9CbdpKVypPsOzcRhVE0nP1mJO2lKXy/5FmNkennJbDbFiWNcWFxMFS2iT6lF4hzRWKiGqE2pbl5zSkRVYa1gSRtr0gMRYmGQnSHRFtQxsFjXLGdrZt/8LuPJDtVwyNwarC2pYsHq9z9Bzi6pgnQEY2VLpBEcllqTkKxoCglMITSbbGRyKVZMSpOzrXZVTJo0Khs9oJb03WgEbUi33ANdPlRLFreM/hWSTdvAVrqx0BGn5HDXNsctf3bMOkjXydkYKwx3BhjSSZE3nkjEOkmEeBOpNdLIimoc8SqcrC9xLvPIqtgQqRdryqICJ1nXBNQLw/GAV187ZL1uGO9WnMwvOZnPEFV29yvuvHOPxqfNOHplNW/Q0ylub4dbr9+ksI6jmwfYwtIWTtlep40RCmsZDCum6ti7sc9439OEBsVTjksmBjCG3RtDdnd3ODzax6jl6cNTLk4XhPz92QIjRTWk0odKTEr/Y4dYOHoFXOUIa6UoSgbDMqWFnV8SvU9pUkUrigfGGvYOK/CK+ohEw427e3x1+Cb/2X/629fWj9bA7RsDHn9yQbnvGIyPEKlAPF9941V2ByWreslNSpZzR1XtoCiXF09Y+5JmvqJZKh9+5wmmcgwXDffeGGD3AtbWSIxYOwJf40eH3Dm6y8XTJ1TjJcNDofCBy7Pvcbka8NXX73J5Bne++gYD9RgxGGexlNiyTAcIYlAF33gKV3A6mzFbzwhe0CZFR66aBdXKIWpxVarC8/jsUybFPgNbEPya4+NzTh+cYywMqoK6UWazmrku2Hl1j8l4wmzZcD6PYDxPVhfYnRFnD5/y9MEJ1WTAwc09jNYc7IwoyttcPDnlcn3JypxjZZIi/mgIuuLk8ZLF0mMcKSKwuV4yfDAaEDHUTcO6rhkNCqwtE6EsNp2iiiRhUk3CiaIQ6gYjUI0GlGXBcDjEubJLs3JVZDpdslwCUnN8usZFQxPnvPrG66z8AucCs7rm1o0b7K5XjMdD3nztHS6mM957+AGDnZLL+QrjCoymal8aNZfJTdUDgyofvf+Yb3/nB0wOodovGQ5LxodD3NChzjPasSzOa1xVcPetPcL3A8uFZ3FZs76wLBae4AVvGophxfoSCq3xmtLwdg7HDKuCei2cn6w4P14wPEwls1fnwsOzJ7hC+K3f+C4XF3PKkWW8WxFC4N2vvs79R09pQuTevR2WyykaB9fah6p681o/sMc/cPR9+PIg+pAqC7WeoLFogGa1TtXYEIwFjWtC06Q0pRzxYtq0dI05ZVHy4ZlibcruSKXgQWLSVJUsHoxKsh+9YmLAmwYfF9Qh4mZJh0ZI0XTWlZTVDtX4kIODI/Zv7LN3cMDeZMR4VDKoCqrCUJhWwDa5n63fsPF8ZctS1XwYmO2l/HsS+TZIsUNV7nJn71XufKXmq/NjLh5/xMcffsT9J0vqhpReKckuN7akHBQc3R5zdPNV3vjqN3j88GM++v73ePjRQ84vVoTgWM8gNPU19qDiQ03wESOKLR2biLaIrZscgS5gk2h/DDnyybT+T3ptFg9NAs/ZLo+tfIKCBrD5l4imcDWbdJ+MNzx4eEYT3udo/5z9wwPGe3u44RhxZY5gSdWscqwiG49yY7MmL7J1BrdJnjaKP/+nqe2dwE5pmJQVd/Yrlvd2mc2POD1d8ujRJdPTadLg0wXGrBHTYCWyMzDsVgU3j8as37zD5WzF49Nzzk7OqOdz6vmCODfUnOHXNeuHF8ynS155/Q7DnQnDnb3P7JHPVz1KQNUgNcR1gw2xFZwg1XmxV+mXHBHTxi1ciSphE33SOlSdI5WdozStQsvhdCfnsV0C2nDG3MDt94SgKUS/vQwxGHE4Y6lciZMCY9s8vXWnd3GlwHZ+zKhkB5/MHunmxF02F9ZO16CyuUttI242149IDvrMz7XpUV2USZqsrV7FFS7rGqDOsPzr38T8/CvYOzuciaf2NVX0DMdDXn3zLtXNGzSP1+iDRwz/5FeQr71JDB4zHBDbE3ZjECzRK/ZggmognEwJQQnrGU9XJ5SjO5jDHebGQ6yTkJhfo80CCQ1oJGSBYEOKatqOrxJN1YJCvU7jRWxaYIuCsigxklTF1Qo+CzdvXPythtuKzErrbHLc29BpbcmbvMSGmAkfyeK3sc0rzwtMy7fFbqjSahkpbZ9uCAL0GULqGtCs15x+/H0ePXlMOdrljRuv4zDE2NDUaworWFvhioJCBBMaoq+xIUUrGVdhFbReo4tFFviOSFXiXjliuYz4dc1ynao1lLVSqCNKJJYQh4Zmx3ImM6INVDtjdFWj60Dz9z9i0ljWVcUqkoUTk66JRk8IDW3ZRjE2pQJpKpvaCgfHXIGrJVO7vPEtclUzeZbmZBu0uhU5QyJjQyZc2zVms5Yom+jA9KlJpb/ts3aO0olSX2cvCqA2JPFqk4ihqIFQlXgCPnrWoaEOdUpjapSmXlNWJdWgBIX1ssFUgjMWNRAbJTaRECJihb39Met1TVG5VJUjAmqIUQjrJo1lY1ADphTGOwUDLfDep/GcTyq6O88NoiRx8kgSEKzrJp2aGKGQVBUAFaqqpB5EjHGIM8zXTSrvWAp2qAwHZS7NbTOBk0m2VjRYwDgBo4x3h4x3xxh1mLxuNHXDfL6kCYAR3LCgHKSyzdEHyqrAVoZQBzRGjDMwvF5nPwbP8eOHGCM8/vQpf+JPvsMbh1/l9z/6ARfHT/no5Jjx4RBrlMke7OwadDVlPgV1DXO/4OBgh7e++hqLsGA5m7FolNFwgl+vOHl6nFbFYDi8eZfzh4/55MGnFJOCncEB1hpOLpc0J2uO708JITKcNlh/xJ96U1IeeC773gpytlUTi2rAxWLJsmiobMFUDK++dQuKc0KzRH1gPp+xe/se4fIpF6tzajtAizR3xQuDvQFnYcFACqgEKScMB2NOHz9htYrMz1aUu7uMo2E6v8/yvEGdxRYFHsP0bIXWSrnn8bHBu4K3336LxdOUcjmXOZQNutNwsDNk9WiBd+5aNRiiRuraE2KqorYzmSBC0lqyLjkfeY+KuXRgOnsxOOdwZUlRlQwGVUoftm30oKLFjLOzgp1dy2iv4uJswY29GxSTA/bGRzx6eMrF5RzVJE5Yh8jQC48ennN+esHJ0xNCHZgUOymEX00++RSMTVVvTAy4aDDOsK7XHJX7XF6uWU6V48eXYCOBFQeHO+w5C4VHs1D4ZG/IdLrg4+895rV3jxjvHeBGSumKFIVTWWIccnkyZ7GsefrwmOn5kqooqIYVAIPxkLIM3NqzjMYDwhImdWSyN2B2HHh4f8a8OWH/VkGoDR/94JxqVHIwKa+tD3v06PHlwnqdUmLSvmOxSE6TSilp3gd8SkdI662CdQVFUWJIGmKJIE8HeEnMWTPhk6I5TNYYSz5XxIhksfBkL5bWUjjLeDigyGLgMSQtVk8NNFCv8OGc09lHXD4qqYYjBoMxRTHAVhMGk10mOxNGkxGj0YBBWTIoHaWzWGsorCFLMGa/VTt/dGNhChoNrVYm2UaCEjO+x8Hbd9i99zXefPpJEjB+fMLp5ZJlNKmKMiBY1FqKvVvcm+xzdO9Nzp884NOPPuD40SlqKux717cvalS0TmLQamCtqdhEDNpV9PUupfhr3o+VrGWTdV59Q6r4pT75gGz0Esk2ZiqbnaJak22ehLFtru63XgsnZytW/pTHx0uGxSN2BiX7exMObhywe3RIOR5DJnBUUgR/cvJyr2grOdAe0tL53l1EVRtQkVO4WntfRLAi7BSpmtbNvQFvvrrH5arhyemKJ/fPWZ5fElczClkgZoFS44zBDg3DQcHR4Zj1G7eZz9ccPz2nGJ6wqg3r44fEZp7ElM8uscs5Owfrz+yTzxdp02o7NIE4v8Q0DbZQapLgl9HWGE43HdnkD5OdXmllwrJj2w7yjTOURbs2LMfG4wbQkJyzLr2ojW5oJ0sql+1DK+5FMqyKElcWWJdyJIEcRteybnSibm2VqjbHtXtOTXbO84NRusiMNle2q4OVR8R2Klc7lVvR0y7dy8TkCMX0yRaT1FZbuYtrhNkZ4w8rZn/j71K+9RrDr73DbrWLP6lpfnAMT05Zf/AYmU2p/tRXMH/qFwllidRLGFdEq/gYktBpDEQC9cMT6senTIaH1NowWxwzXT6l+qt/Dx2UrI+fUhKQLLJUiWXghTrkCk6mjZTKo6QlUAQgsq7X2Wk22GJIWZTYosIYSys8Jj6Amo70kZbVJaU1bbc5rROq2jnl0LLiuc+kjcLJF7VdASqH+3VLs9I5/0n/RiEa2iUMlNBVKLoeiMDh0T4rv2AwPkyCrg1QB5wHGw3WCC4IcX6WhNqWCwgNRMVYw6r2+DqlRhkn4D2FMazWnma2wljDZGdENRwAFlNV1L5huL+HPRyjKM1yzfjmAZUKs/vHFMvI6vc+ZGAsWg6IUXOFGyGqp/CpjGcbMmmsxdkCIyl6y2h2xkPA5AU85qiXbX0iVAm6IVhSo6Ru0phS0hJpmnJqg8aUNoVBIoS4+ez2M6LGVKa87bbWKcyq9lGV2l9f7KlC0kgwQrBpzGGUUJH6IAQa3+CDT/cVlNB40JKiSIUq18smnTqZnP7jIyGXqjUobuSSpo1LMu7Bp82xaSJNXSeqyuQTCoWyLAkI6/kaynRqkovobDa3jgLLxCaRxteIDRSFA1IURL2KFFUq1apRWMxrptMa5xzGRNxIGO0WDAcVhXVJ8yYm4cFmlU5wQDAuado4jbiixBqXc9+VZl1TVND4SB0DtiyohiXWGerlOp/iaY7aU4LWNH9A6OmP1o/CYGAJdWRnf5ezJ0/44O9+h1vv3ObifM7j8xlvHk4YOIsPM06PLzl5vOLuq/fY3bWs13POxXLn7m0+vP8hB3ducH5+jOgZwQcuLiNRAjf2blPWjqenDReXyuL+gsPbhqPDHdbzktkysp4vGYzH7O6NKJxNopqOXGEuIeXtR4yxWGvZvXXEcOeEYSg4GHyFdXPC8dNLTig4vDlk+fCM+jJmcciC2arh8umc/XuHTPZLZssL5s0aMz6iGRtm5+c0H0x5/MkxpiiZ7E04OnqFe/tH/P7DT5NAYFROPj2jMYFqGDg+PkefgjmEi5MFVSzRZeDRwzPKg5LdOw4ZjhlX+0yPZ1hZEeL19WNU5Xx6gSuSEHdlHcbkag4tQZk1MdSGPJeSsK0xhqIsGA4H+f3JMDRG2Nkt2L95yGw5oxp5VnPPrVu3eOXuqxQHntMfLAmzgDaGdVAuT1fcuX2bj757n8MDT1OvGQ8Lji+XLKY1Yg0S7ZU0LcniycZEJpMx9+7dxjcLnpxfEuvIzmCX4aTk6dmSp/cvOLoxYmevYjUvUsWjdUNpHJ5IcA3iYDzc48bRAfV6ifeKZcD3jh9ydrGgXnk0KpPDMYNywPnlFLWR8aslw0GJtY41kTt3xwxHE4zW3Ll7k8ObFednp9z/4IIQLHdv7vLV1169tj7s0aPHlwez5Zq/8Xe/g4+RonAMBwOcS+n7dVOzWMxZzlOarLUW5wx143GupKgqNIQu9cYagysKrC0oCsdyPieEgFiTHyuwzqbDVBFiCDhnGQ4G3LlzhGqgbjylSxp31oAQkxZVyJEeUalDwBjLoBpgiyIdTluLwWLtGFMOGe3uUQ0njCZj9g92mYyHDMuSohxQDQYMhgWDwlLaZINtaAOyH5ErXRlAUlGetmC8rQ7Zf/WA/Vd+htfnxzz9+Pt88vGHPDheUFNhcrWrJH7sqMYH3H5jl4Pbr3Nx9oTzR4+xf+t3rrUfFUWNUMdI9A0xpPXf2USgGe8h68+myPmOBUlkWw57CLrJhnBZOD3kQ0QMqPX4HAQiuVJYExqMbwjRMlt5qmmrYZUinyprGFrH7nDA0eEuN24fsXfrkNHuHsVghBRFitYhVXRLlQAlaY62PplmUe98yN7p2gKdxauKidqRi06EorJMKsudvQH1m3ucLTz3nyx48uCc9eMTCn8GdgWmRvApRassGVUFNw5G3Hv1Nm+88yoffvCQT7//A9ZP7jOfBtYnc55+Ov3M/vjckTY+Nim8qVljg+LyCXdUUuqQbiRgEXnGqZIuQqGNrcm8HC0n2Z6ci3Ya3ls+WctMJk9aOkKk/SW/Pqa68J3jJYJxBdYVKeQsE3Bi07WQ0yQlE0Wt25+Moq3vVzrl2nTP0vl16amrolTtM9uJYun0vo3AyFE3uWpBe5JnbE4Zu+4SfIBaw+Qf+5P4v6nUP3jA+PGSnZ3bGHGslpd4P4ejIfzxn8d8/RuEsoIQEAxmUFGbJACXwsVT+KP6BndzjEbHarridHVCQ83OjUFiZY8b2kmjriAYQUNE8oIsWfOlTUkCkgh1HhnretNBxpZY57rqDIVxFNbiG98RLJl6AbKYbXa4TRfZlZxwyU79hljMJKJCJ5CdJ7BpIy7aKmHaxvWkfkQFMUlcuYvc6BaAbRnq64ErKyZ332DfjlJa2jqTE9YgpSMGWPuIbzxm7dNJhApqC6JRxBjswGKrtHmISxEvxgqrDx4R6yWlCqUMsAxRZwlaYLRkQIlb1/jVCrOo8X5Ok4UCXTWAssBUjmkz470PjnlcKkSlKAv2zub8Q+fTjsUWSd+ZKmgkNruuPR9+533mTS5ZnQWhM8+WKiSJdsKRrSI9rZAk7cIvmXBNkSSSyVeRdt6mCJ/Uwelz26wo060ddPNZkVQG+5oQVVOVrnxEo0axlaU2MbVnExKrFBX1iqaANZplII5SJE3QJLRojEu5wi0hHsH7SOOT/pIxSUDb+4b10kMoEBV8k/KVRSy2cIhYokKoteWoc3uZjRBxy3vmNdQ4EALWRMpSAENTK6t5JIYCXyuanV4fAnFgcZVQji22TGSSz+MjNg2hrokkg8wVliJH4qyWa0KTxEBdWVBVDjGa0mWAddPQijj7EBM5L8pytUpC8zETTdcrhUIIkYvzKQd7Qw4Gjp0bI1YroWlqxmPLTanQsOT8WFicB8QZhvu7zGYrpgvl4vwph/fW7A3HWFtw9vic2BQ8PDnn8M5N7t26zfHxgnt3XqFZnzKvLxmNDfXScXnacHl8Auo43JvAzghXDmnm8PjhBbTl0IG0r27mirM2lWZfLrg4PeVkJbx2b8yjBx8Th0lc9fuP71PPa1QfMdobMhyUeDW8+bW3mflpEsPWmunJgvmjwM6NEQPruDiZ0dSCsyki8+LkUz5YnWEnjsXTOfPLFMnF0ynlQHAIkcj6sadeWp6ezynLgnkdqE8a/AqGu55lcYmKYzlfUa+vryPf++Dj4//Gn/8XPrq2D/wC8Nt//Xv8+//ef/rFfslv/fAvfe/3H/5YX/U3Nr++8WN9UI8ePb50uLy4OP7Lf+k//1KvqS8prm09jRppYg1q8T6JAHvfoL5OJbfXJqe/aedjG0xKn9J0uOGDx2KJJB9FTOYKmqSPm75HgVQNN6jSZCLNiKEoHKNJRTkaU1WjdFgPKW0uRhYSWMcFp8cLfnD8iOo7BcNywHA4ZDwaMdkZM9qdMNqZMBgOcYMK68okm2KSCHTy6Ns4/FbPVLogjdZ37yq9YZK8af6rEuHOpOD2ZI/5G7t89OgGDz+5ZHVyDuspcIGwBK1BPSLC7tix+84h917d5fHX7/HJh4948N0P8E8eYfw1kTZBlHWs8dHQrNeE9Zo43mI9tHNXN8ExXWCDbB4TsthrrguTPK9NBARbji8t0UFOV2md6k3zQk5tkpRG0Z7ub8MYh7EFiskpGRZrDE0mBjQ7eZ3Dv8lrIXts3bWk7yOXRWZD9OT0nlQFKRMBrar+VrTQ1biSrbYiCSIZQxLxUjC50Pn1QdHRgMkvfwP52hvYWUS0QozD2UPMzpAwLgnjCeqKzHZ7VAy2KomWJBJqHLUIwSijr71K3BkSf/cx9dmChV9RDQYUb7zGfH4BT08I0zrdrjEpVK6tYiVp7LRJc91VqnY6QX7tk/MOYBzWukRCZPFaZxyqi+To6TZlk0nENpJG2xGjbbBMKu2eO7clH3MzITmyo72s0F1Xfmir37gijpveFFsOqh1g14gI2CItPoIQbYSY2in4mMQz64YYmqS74HNVL9IJrYfOkV2vFym1xaZsz9nFI9zZMYVYrCkwS4cMKrQsiIUlGsWLJ5qaMKiZLaaspWa8UzEZWkonqHjmojhn2XUpHLIcVuztFBRWNySrmFxxSlLEh7UUZcHd2wc4lyqUpTK8bTnPHDVl6IioVFYw5/+2wXGZsU+cqHRlu0WTDkRL5kpOhUsHHS15R67clkib2K7OCA8ePrrGXlSKocMVZku/qeFytcSRKjaRSRNXOtZNclKdtUQfca5gb3+X5aJOgs8eQp1Djr2n8Z71PGkGlWNPNSgQDM06VYcBYTH1NI0HTeGuRiSL1xqCKM1a0cx4CZlw34pANAjWGqrKpVQv1SwWaAmrwHS+RE2gHFrMIG2KpjBYZ2l8w/RihTOesigYaIqIcQOHKxOJa8RhbQHRUIriTQ6HNikv/nI6S2RhabFZQFCDUi9qRKCalITgWa8avE/XVj+zN/y48D7y8NNzZrM5Pig7O7f52juv8uT+R3x6NuP8fMGTZs5i7onRcPOVPV59Y5d64Tk4OmQ2O+Lx48d8/OgjBoNd7t26x+9/5/s0Daw/POHWvTGvv7bP6uljfvDxfaYzZb5Yc/O1A5rTho/fv6QaOsKqRtSyd7PgcjFl7+CVRIa2VTE05ZNLXluRNAZOnyz4eLni8MaETx7/gPsPTlgJlK4AUVxZEGPk/HjJeRMw1uEYMDoYM51NMWKJFmxhuJwvWEyX+DpSDEp2d4ZMT06Zn1vmhxMGQ0MxKTHLSLPyNAtDs4TCFlSmoF5HvAgDt8N6cY6pIl4CKw9mAR/7Nb/wi1/n/d/+ezlN6XrQ62j0+ElCRP4d4OvAf6qqf/EnfT09Pj/6PryKfk396Ue9XvPgww8xxuJDpG4a5vM1q+WaerVOx90mVQorjGU4HiZ7oGmrK5KyAIzFuHQgWK/W+TBQMc7iyhKxFutMOkyNgjWOoirZPTggBGExXbNeRRZ2BTk1PsQ2ep4UieUMomSdxSHVcIk1FxjAGZNS2irHoKoYTVKk1HBnzGA8psxEjnVFOry0LlVJTTXhAHI0DmzzAsrmADj5kMLECN+4N+HtV8ZczG/y8NGMp/fPqM9PiM05wgylAU3+2tAJb9yZcOfG27z5lbs8+vgpZw8+hW+9+ATl86VHhUDQhohDQkDWNRJid/q9jQ0xkTUKZGPst9o0HQ0S23ZoqZo2JyhCzMJP0OkrKC1zw4YU2opK0RhJEtrtRUlH1Gh24FwuA6dwJadJ83G+tAK02bNPuXCbsJo2MsNk8iVy5ZI29x8N4GmrSXXpM50watwMBHIUgZFUxaZLJ7s+fHj/k+P//r/0P71e9vujv/Tix/8f33z+sf/zt7be963nn38R/s3/APjnP+9VfRlxrSeK4nKpTlWCBIKmclCRgIrHu0jj16yXC6Zn55ycn2AsWJM0Yxof8L7BWlJai0m6IitzQfG6o3QGYwVMRGyNLcBIYs/VgJECxfH40ZLTWc1Xv3ILWxbMGs9yvaYaTDgqdzkQyPVy2bt9xKB0pAirzFmblCaFSYRqUTjGkwmjwQCJgrWCM0LtfUpXUEnEJgpB88Kd5mcUQTXkql95YcpC6WjsxONasnUTSJP+b2uKaRcNSHIOcwidsdc3G6eXy+Nf/7/9rS/5SdTfBv6fP+mL+CJwbXPRuQLRAfPzgDjht/4/f5/dyYhYL/n0ZIYtHauVxxSG3b0BNpY8/eiY6azBasQOJ7z77h2ms2NuHo24PdjhwfcLPnxwgSlHfOWdEbPplEef3Of8bIEUjsUscPFkiRVL4yOL4wWQ9taLxZqjO+OUmpbJTAHEmDQ/YkxzQQDj2Nk74M4hTHbh/vcf0IyGDIyhXjaMBgOefPIEBHb2RhTOML9cUN+BAYGDoxssHj5ivH+EVcP8ZE5sDNXQ0aw9x4/PCd5jjGNnv+TG7bs052dYb1M0jlcGuyVWwZGMQjeZgAk0VcP4aMhIBnzyrRPq85rR7X1cUM4u6i3SvEePn16IyD8DWFX9FRH5N0XkK6r6/Z/0dfX44dH3YY+XEU9PL47/9//uf/Qlt1FfWrzQRv3cpI1RJWJxVYWNYEOOYNjSs1EVonSyoG0YCy0z1VIzdO/I4kA5nYGtqAsjbbFwBWxKZ9qOhNmKWEkETjpNDOFqGoPJYfNWUhWglGplrgjFduH/+cLbyAxIUTVkvY0OLQnVOny5EpF00RubEuWmcxK1E0FNX20wBFq/Nt1O1lKJQquMcl3o2e+XA9/79reP/5l/6p/6Ui2mf+1b3/mhXve//Tf+3S/2Qr5YXJuz38/FlwMXx/Pj3/h/fe8Lm4t/57/47ud7w8fwA+C3+R7/9//jf/iFXNP3v3X/c7/no+88Av7eD/Xab/7mZz/31/6TLme/T6vp8TLgV4F2ov5l4E8DvcP/04Vfpe/DHi8Zehv1ywfRz5G2ISJPgS+Vo/hHBG/0k6dHjx49evTo0ePlQU6r+T+o6t8TkX8c+OOq+r/6SV9Xjx8efR++POjT3Hp8mfH5hIh74qBHjx49evTo0eMnjt7BeCkwA4b59wmbhP0ePz3o+/AlQJ/m1uPLjn5h6fFTARH5d0Tkb4jI//wnfS09evxRRj8Xe/T4yWPbwQBeEZGv/KSvqcePhN8hpdMA/CLw4U/uUnr8iOj78OXAr/J8mluPHl8a9KRNjy89euO0R48vB/q5+PKgJ99+6vGr9A7Gy4BfB/68iPwbwD8L/Cc/2cvp8SPg1+n78GXAGHiQf78Ebv8Er6XHj4iX2bb5I0HavMwd+EcEv0pvnL4U6OfiTz1+lX4u/tSjJ99eCvQOxksAVb0krat/E/gzqnrxk72iHp8XfR++NOjT3H7K8bLbNi/9gHzZO/CPCHrj9CVAPxdfCvRz8eXAr9KTbz/t6B2MlwSqeqaq/6GqPvpJX0uPHw19H74U6NPcfvrxq7zEts0fhU3+V3mJO/CPCHrj9OXAr9LPxZ929HPx5UBPvv30o3cwevTo0eP68Ov0aW4/7XipbZs/Cgb3S92Bf0TQG6cvB/q5+NOPfi6+HOjJt59+/Dq9g9GjR48e14I+ze2lwEtt23yukt8/pXipO/CPCH4d+Ksi8grwTwD/8E/2cnr8iOjn4k8/fp1+Lr4MaMm3v0ki3777k72cHp8XqnopIr8K/NeB/03vYPTo0aPHjwdVPWMTEd7jpw8vtW3zR4G0eak78I8CeuP0pUE/F3/K0c/Flwa/Tk++/dSjdzB69OjRo0ePDr/OS2zbiKr+pK/hC4WI7AJ/FfhL5A7sHY0ePf7Bo5+LPXp8eSAiByTy7Td68cwePXr06NGjx087Xmbb5qUnbeDl7sAePX6a0M/FHj169OjRo0ePHj169Pjh8UeCtOnRo0ePHj169OjRo0ePHj169PhpQy8E2qNHjx49evTo0aNHjx49evTo8SVET9r06NGjR48ePXr06NGjR48ePXp8CdGTNj169OjRo0ePHj169OjRo0ePHl9C9KRNjx49evTo0aNHjx49evTo0aPHlxA9adOjR48ePXr06NGjR48ePXr06PElRE/a9OjRo0ePHj169OjRo0ePHj16fAnRkzY9evTo0aNHjx49evTo0aNHjx5fQvSkTY8ePXr06NGjR48ePXr06NGjx5cQPWnTo0ePHj169OjRo0ePHj169OjxJURP2vTo0aNHjx49evTo0aNHjx49enwJ0ZM2PXr06NGjR48ePXr06NGjR48eX0L0pE2PHj169OjRo0ePHj169OjRo8eXEO4nfQE9evTo0aNHjx49evTo0eOnH7u7u3rz5s1r/1zVze8RWK8bQlNTFkJVDVAxBIXFYg3RA4qIImIQEVQEAHnmc0Uk/TMG0/4uJr0wf6c+cwExKqravUDy5xiz+XTd+u/q82b7FSDPXlG+xq371fbB7UZ45k6ePn3KdHr5/If1eCnQkzY9evTo0aNHjx49evTo0ePHxs2bN/nX/rV/Pf0h+b+OwVA2ZEP788VEhF4hKDZQYBWEDz96zOmj+7x+0/LGV94lMmYahd/55vdgdYZqg7VQFCWuqFDrUMBakPw9IlBYiytLiqKgLAqqqqIaDACHRogxEoLPV6eECE3d0NQ1mskhI1Baw6AswTkQQVXREEEVa6AqHMNBhbHF1p1IJm0Eze0jvJi00a2/NvzNhoj6l/5n/9If0jM9fprRkzY9evTo0aNHjx49evTo0eOa8GLCpSUZnn1WAKKC6BZv8+KgEQGMCMYKYoVIojHaV1tnCcaggUS6IIQ2gkZBAogFMQZEURFUE0kUghKaQLAB6wyoohoQgUgkRCVqQPGICR2xFFGCRnzwOBEwggio0cSuCCAp8ge0I2o2iM/crSSuS+PWXbdRPdtkl/6Brd3j5UFP2vTo0aNHjx49evTo0eMnip2dHb1540bngMoL0kaAZyIQrrqrsvXb1WeS89x4z2KxZDKZYK1BVfG+YV03nQMuYhCTnWpJTnK6lhQZIUYwxnapNJuv0KsutKYUmu5+SI685M9tX9NdsaTPls8gK34sbC7i6sNRUZSPP/74WFWvMacpt9cL2QQFTc+rKKKb+JLtyxPZbp7tKJ3UjtYarLMplQlQUcCgKsSgxBDBKlYDQsBiMJL6rf0iFZPoEjH5vVA3gaBrnAs4a3KgkKBi8DGiTboOYywaFaJiAEu6psTPCGraNoj5fiSRS2300R/QcrB1711w0obw0vwi+YLYmuFgoHt7OxiBGAPeRzBCCJEQIsYYYoyIEQrraOqaGCOqSowR6xyj4ZBmuST6RGQZs+lfQYhiGR0dMp6MmZ9dUM9miCYKjtz3CmAsMhpTjUY0l1MMEYxBigIpitSfCKoxEXFb7ZTS0mC1WDKbz4ghPrdmfC5ou66kMSPWYVyBFCXiClRM+sJnUvHaQLMY03iIz0yNPBsAWD58/4Vz8XORNlJO1IwOU2iZwI2JIYTIYh25czCgLAouLqecn58hAs4YrLWb9xuDiMHnULHtMC/YhMF1Y3Mrj7Bb9FRRjXjf4FyBMfZqLuBW1N2moaTrtG6ym8y4kuapEcHkRdrknMbNYs3Wd2wtKLKVBylbn/cZm8xznaNbj3bhg5s20Kis1ivOzs5YrVZ9jmKPHj169OjRo0ePlxI3bt7k1/6VX2MTifBiqOoVG3/zxJbzk9/fBjmAEmPgyZPH/M7f/hb/yJ/+FfYPxnjf8PjJYz755D51E1AENxhSDIcI4MRgjemiMsRaXDVgNJwwGgySky4CmhxWYkyRGSgaA+tVQ+OTo2itpSwKSufS8wLEgIYAQFEUDKoBztpNDEVmLmRzw1sO/LbGSo7IyL5IR0A9y5tk8ql19hvf4OuGf+Ff/B9+9EN20w+Jq9d3xX3V9ra043UkP3jFNfoDGAkBrLXJF7R0PJCQommiKlEVo4qgW1o1gmASRyOAs4TYRtrk9xEJ3qMaseLAOjDgI3ivECKmvY/kpGJQrBisc6CWLlImhBRBtC1j86Lbes6V3RbTYdNmL2zb7XdeD8ajAf+tf/LPcvfOEeia6fIpS/WcnS+ZTT3ODWlWS4qy5GCyy+OP7zNfrFnXDbP5kt3DQ375Z7/Bk299m9XJUw5HFUNrqazFIDQ4Jn/sl/kn/vn/Hme/+x3+zr///0aaS7xf0ESPsRZxBeXhDcav3IPX3+LOV79BeXmK1JesmoZ5cLjxAVU1QFBmixnV7g6ijjbhzFpDYQ3v//3f4zd/829yPr8kqE/zxZjNfFJB4yZyKhGDLhFGpkBMQTAVVLsUN15h+MrrDG/chdEua2MJmjgEUU1rkoLESFzWrFYNs8bT1AEjgrOACLElDlvfX9I1f/Mv/LkXzsXPR9qMDhn/o/8TNAgD6/nz//AQi/KXv3XO//jPfYV3Xn+Fv/zXf5P/y//1/0RlDTf299mZ7GAlsZ7lsEKqIbPpnBCg0ZgWU40IEe8DQRVDmlAhKj6z1I0GTIxIVNbrBafHDzk4uMVw94BoBBMjGgWDEIlEqxRiIAoBSYusEwyGUiy2LDBFSWEcrhQGVcHAlVTDivFoyMAVWOtwzmKsYI1JAlbG5quLOGsQZyjLktIWlK7AOocxQhBw4hLFo4EomthsyRRSUKJJIX0mphmsCKohkVIx0jSeD37wPv/Wv/1v/yjz7cV9WO2oHd3If5A3OOVoJFgrXC4Ct/dLyrLg4mLGxcUZRsA5izXtiiMYa9PiFtNGdCXtVK+efFxlyTcEnMaIb2qcc5v8ziub0vZ5SfszE2lCJyy2Id+kE/kyxubntk805Dkb4FmyLf0uVxbP7auXZx547hRn6zeNkcV8iTGRoqz46ONPrvkUo0ePHj169OjR4+XA1bPpBO3c1Gce73yrz3p+K7SkYy4EMRZscpjaCBprEpmiMTnmIgYrFoR0oJudMFVQA15hERqiF8ZS4YxBTU6liUrrjSS7XiGkrw8a8RIRo1gRzFaESfdPUqrNlXvJzrqBq2SUtASW5CiOrba8GqaS3r11Iq5bpm78DO2YHwe6ZSRfsb1zP3TX+Qd9wh9wWSJgjcFZh5jYkT0m+yAhiwTHK0ECbRu3/gKotYSOGIkdCZh8G83+Ro6y8YnMMTHk+4uZfEvXqiJEkuBxyvLaRE2l6JsX3eXzf6S+3D7Ub5+Ujq6R5954vWf7kQjOEGK6h6Vfcto8RSaGYeVQrXH7nkEJNi4oh9CoJaJUTclkWCGhwRU5gCOm8RkRxBUMb9/ij/3pX6Z58pi/+x//Z6wX5xgbEGvBCF4japRb777G7r17nERhtl4xf3Cfhx98l+PplMHte3zj53+JSkqCDywWc4rJBJu7ecOPKUXhsMaSAqNSxFMb7qKQInfcIAdwlIRyhCkmyGAHdg4pD24yOLqF3TskugEhCssY0vhqUkAJUQm+Yb2qqb0n+pgnbhpzzhmMpqisqBHJ1JzJ/fyi9W8bn4+0AWIUJKTIlNUqcrBjaKKhbpLzPh5UiEaaoKzqmkETwKbB5GPE+AAxAAZLYipjIqYxYgmiaZnLi5HRNBFKMQSjGEl5hV7TQmgUJCrEtPCppAkqMRLVg7EYNUhmR50riCoY75GWNFGHKoQY0BiI3qcmtInwQW2+QIOJkZhZaqNgW4EqEwhqMSGx9NYISMyhXel+2wkOKWRONU2KtLwbJLPCMYcqBo0UtvxMIa4fBXZ0g4M/82tEA8GkxaYM8Oe+bvjqvYL/6Lcu+Rf/7G2+/pXX+c9/45v8h//Bv0dVNNw63GN/ZxcjKXRwcrDHvFbq1ZoQA+sQ0kCPkRAC7TiVblIYogZiDARNTGa9WvH44SccHOyzf3SHiEkbR95JOkJPSMRZ3jiNNWnyWYezjrKssNZSVUlAbDioGA0GFIMBRVlQOJOip7oIKrooKWsdhbM4aykKi7WWwjqs2BQamxdONSZvxBuoKhrTT2PTKUuMSoiRGCNN3fB7v/ttfH3Km2+/y//gX/wfXdspxmA41NFkjFG6EMUQPUgaq865tJDkRaFpGpqmoSpLjLV431DXNTHm0wfa6C/Nm7dirUthfza1W13XhBgxxZDIJsLNoBQSsLmPVBVye2nw6TSksIgVQhOoG59F3QLW2u50SIBBUIzGbbuiQ2uQCIARitGQYAwxwsJHEIeqIYaYQzTbEEjTjsTc71uhk/rM4igw3hlSFob1smG5bBKdmq0j30yJYdlHvfXo0aPHlww7Ozt648YPcy7yotP/P+x1Lz7h9t6zXq0ZDCpcFjldrpZ5D6Lbc7oIbNmO1pbO2ZaWHGgrz2wdfnXOr+ozjrDp3ttdn2omIbaJgM2tbjt6+txvL2qLP9z+PDk5ZjqdXsu+2LaZ5vvdjlxX2uiQq4drG5f8Rb+lm05vSw64FYMTIfqwuTsFjYJqPviT7IQhBI2oSGc/Cumg2IQG8YJal/RLYkRjiuZJZEHM9q8SJWYHMuIVTBQkk0LSuZb5X+erS0fW5DvZ3J227bU1Rp55zZUmyOO2HSPbL/5CUrG2v4eWYGqvdXOJG8iLHvyMj9z0sjU5HWmrnZDkP8UcYRM1orH9m24sQJva1j66+ddGSqS/UiUpVUPwTapKpeGZ1yfXJaLUTQBRLOnAX0S7Q2X9zLbWbRZy8/OZMKnu9p89Qf4iYKAoIEbPoBowKo84nZ5zvjxnXSurpcc6Q1VMGQ0GMDAMq4rRZMzuBI4mu/hZjZUC5ypUCmxVUVRDBgf73P2lr7A7KfnuX/orcHqCs55QWNa2YO1roipe4NHZGZ9O11yYIW8NDvnwex/wm3/tb+DLkrd+seLdENJaagzWFcn2l7ghzHLz2Bx0oRjI6XRiLFKUqCnQcoIb7eKGO+hoDzPex+0cUuzsYYYjIoJXaKISVw3BJ18jBsXHgG+SnlHI83N7Hda8Bpk2cANeKN30h/n7n0/TRlNUiCWxzLNGecUl4mOZSZvhaEhQIXrPdLmkKComw0HnGGqIEA0iETSganI4GtQSQBPzHGOKTimys9QkGgxrHAaLiMNrwGvEREmOplV8drCMCgHQECjEIlGxAhIixlisambBPRqFGAJqhBgbCAXBJFrcYJEYU6JiVMQooiZx6JKIJmJWCNeQ8viMg2AI5HBIA6qBxCzlhaD1SvO9qliMpvzLHLuCaAD7bE7ujwchEWEpHTCHAyJMV1BZgxHDap3InN3REA2wjjWz5ZLRYNgZJSFGYogEYjqtEJPWFk0kXOpsyWmcZrOdZMMkKmAtKhYf0oKqpA06toupppBCRYlqUIk4a0GF4ANkNpvgKQRcMEl4LKZoJQ2eGPLph6Q8QslztW3TqJEQ08IaYyJfQoxgpYv4SgM0ZDGztiE3JxiaiTbIuYq5DGBUxbiS1czjfX2NvQjjyYQ/+9/8J9gb7eN9zeX0FDWGuPYMywEqsAoN++MR5+dTPvnwPe698irlYMRsMef997/HbLFk4EoKEXadMCodD07OWfvAcDDk3tvvMFuuWfmaP/En/ySf/uADPjpbciZ3kfwdRiyqwsiseLW8ZGfoKJ3Fh8B475DVckUzm3L3rQNWzYxPP3rCxcmcR0+Pef3VVzk/P8c3nsYr7yC8Ml0gIaAxgsbMSKdxipFEVjvL5Off4eYb97hZ7vO3vvU9/vZ8yq1bh9wdHGLqEd+ZCWeLJbPplNW6TrnNCtaWODMGMwZTEDMRKFhU4sbgRbBDZQAIEYmeKHD64D+6tj7c2dn5QkpivggRYT5bUUpDNRqAOHxUVusG9Q2g3ab3IkOuPWkyOcXVdA5BQheGTTqx09gaM+37toyQLYPTSFqH2nz0z41tu/QzbKFn108BPvjgg2uLenu+H686ddcJVWFVNyznMw4OdkEM3gfWdU3M4fXybHtuOYldH5qcDtydzOpWCkG7prXSjvl9Yroc+m2H0pq0b+SvesE1v+BGfqgmes4y7RA1cnJ8zGw2uxZvY2dnR2/cvPkZp1x/kEP77Gta/OGXtVytCb5hOBxgrSVEZb1eE0LYnO4+k/7dRZpCFxmKtI5BJqK3J8VWVOv2XUjrgLb2bEcCcOWz/uB7+dGIj2cf/fAa5+KNoxv8y//yr73gqrbtKM3OezLfE3EvrZuWX3J1gGoei+0q1zrZinJ+es73vv8e77zzFjduHNFE5Vvf+j1mi2mybWzBYDzCuRKxWQNFSXIBRrCuoHIOkw+KnHMMXNHZQooS6ibbV5GmSeu1iFAN0oFV65zEnJ5TOcu4qrDWXZn/2bK68veWi9rd3eauWxtn8+qWNGHrZOXXfu1/+bn76rPRkiX5r05jRra0YZ4de1tj/znff3uTUJSIMVA4l23BZBt0vrts7DyDZqKm7X/JwSqpbTQqwUcaExAMVhIpk8ZHsmNiiNlWivkgjXTYFxowMfWPmORIPuOdt8PQPDO9VDe90975s9SL6FabXPnklsDaeq2Raz0c3rrS9hueeewFBE3bTR0X9+xrnr2+TcqTbSPrSX0XJfmU6CYFLMbY2ZVIIudStoCBEFNKSyRlbmhbyjsS8ntT6lubDRGuXE27LqhoGjshp8cZcHkO8gLCRq+0xRbkan9uWVNX1vaNake+n2u2O1SVs7MLzp5eAjAclxRxF1kvsXjiKrBaC3FQUewOOdjf5cbBEUd7R0iosKGgni6ZFHssxueIEQ4P9zi4vc+NV28z3K149O33OP7e+3i/TIEWriAMh9Qr0LUSMcxmnvJwxGTnAG0aXnvjdf5//98188azXNdp3SOtnePhKLna5tl7AeMcxg2w5R5UIyjHyHCC3dlHhrvIaBcz3MEMxsRqkOVcAqFpMLMVihDywXwibNIa0B505x7CIkQDaOJH0jTeHI4nEpetsb49K/5g4+jzRdqoUAWLN5FgYL6ODI3DY7lYNiiB4XiMq0rqWUPdNEwXc4RI4SzlaIyPyclPTlgiVZJDrpmsiYgmR7kdgp6c96mKjwZVCxFCiPgcZuTx2OhQMZuJGxOzFgAkJkcsKkqgMWCbgBVFgxDrJCYkAZrGI85RRoPzAVxq3Q0pkSJxVAwqHhsMmIJARJxgYsgpo5moihuDWKPm60mNYDID3IbWpduMxBiQAIWxn2H1/mhIy1BM0Uchh/ChzJYBl1W0poskxrYzHmKMw/vAfLHi3E0ZjYZJlT0mISrNbWJIi1xityNRN4ZNQJLDnBeZENLwdOIw4hJRlRfJlN/XUY5Iy4xHBZvaJvkmkog4VaIPREn6SuoiMXiCr3HOIjGAGjQKYpJDadsBKCl8FWNy7msaU4llS7O+c2nyNbdsfntdIcZWXyzzcVl0LianpygLmsbjY7i2PoRE9I3cgBgjy3qJOgcq7I2HRIEnp08ZDceglqdPPuXWzZsc3bzDbDnnve+/z3S5oLSOQgyFwOHRLsfHF8QQmdiCN45u82S2YDQsuXF4xMP7H/P6G3dY8pDLuSPkDShoMmAWoeTTeoeb8YyDccV4NGZ9ecFgvIvZc1zOFqwXS8I8UNmCvf0dyuGQeHKCKwru3j7g1sWcOJ+n/pG8ZWkmYq3BKHhVwuEexd4u4WDCt99/wN+6OGF3f5fdsOZf+KULGi/8r//OkjouGY+PWNcjLi6e4pdnrKOhiQ0mzBCxiC1QOwBjsWJSamVM+cwuh856gGjSgnKNuHnzJr/2a38BaP2yq46SSEtuSHYTrho46Ufs3t8dnmXrc/tEqhHHb/7293jFPeKrv/g1ghxwtlS+9/59lhePEPW4wuKGQ9S4zqmzkk6JjDEUZUlVlgyqAcPRKM+THEoe0qmToDRNYLlYkWjzlEY6qCqsLdM1a4pqFCJDJ4yGA2xR0iZ8P2uqvYi27s7LdKuNOuP6WUPoeePvz//5P//RH9w7Pzxu3rzJX/gL/2r+JuFK8JZunL2rZuvGEXnWnL263kv36mSUGT58eMJv/fX/kv/2P/1fRdyI44s5H3z4MYvZBRojdlBSVBWW7MzZxFSLgC0LqqJiUJUMhxWuSn2SBBsDVgViIHjPsqkJocGgODFUVYV1RdvsiZRG2RmVDKphOjXOl3y1CSTv+Zu4t4091RrH7fqZ1tauMZ4z4AER1ssVf/Ff3TjnPy5u3LzJv/IX//UuJk9hazzF7hq2zakX9dVVakC697R30DrIEeG7P/iYi6eP+flvvMNwd5/ZfM1773/IxflxandrGe7sgitS2DabOemMxTmHy89VhWNQ2nSoItIdhPm6wRhNpWmDz/cilIWjdGWnM6ianJLSGSajQdJk6K68PeXYvp/OTaFrhpYA3CxE+fe2TXI6fLdWpc/75/65/861zcX2dLxbHmRjh7SzUNu1Vts+2vTW9ic9D9nqww1VYGzqi9aBMrSn+8luEavpUMvYTWSMkRydS3IGyXMqGSEE51JKPkKzXhNqfyXKo319YVPaejozygdWV9Qt45arsLnDz3LuNk5FvkfZbgnZ+tFa2Z/9WT8uWpLmD8czr+kOEF7gxKqAGKw1ONf6Gd03pmPcmKLlk05JOryzJv3U1tHaCm3SoDSrmtiEHK2dqxGRDpNDjGn9NabbJ60xqe/alHwhBW4gm3X02fa4cre6ZSZr/lO7Dnuh27e1zj4z2F/Uij8+rnxgGw3Wrg0v+sKrD2zIuhe9blvqQLpD464JFBCT52Ie11skd7t/aSZwLFCYlO1AJB+c56wIVdQHxKa9wBpoyD7JFUKdNFYwOGeJweNsiqyI0la32vzbvuP2GrsWaG03eeGz3Zu+EJ5tC/Xa83vfeh+jFo2RoirYP5ywd3SH2i+Z+3Nu7B7y5ptv8fWfeZNbR3tYDFEtg50Dbt++Q1iuWU4XzC+mSGFAIqenTzlfrHj4ew+YfvcHhIsLjMRuv2+JaxuVaB17t+9y42s/x+XFlNnFBa/fvcvN/SNWFwtCY4gBCmepjIGyJDpHUVhQCLnhgxea8pDytV9koJYw3kNHE7QaoK5CbQHi8Kr4oIRVIMYav1wQp+cUgzEUA5KYSy5y1nXPNiHTjtAN6d2+sH0+kglCIQe0bJVvV/0DO/bzRdoIQIqSMRHWK0Wc4IxnuqoRI+xMJpSuoo5TTIisVyumIVA4x3j3kLWGTNDEFEqogm+ZaQ2omBSpk6Me1CSCQIkEyWlPoogkskM0okZwYpCoaGywqhSYlG4TQCRQaHreSEjsmAE1YILixSASUqiksVAoooKJBlRySF9MW1S3HwqIEoxFgiXx7EIUw1ojYsESEWy61nyCHzVFC3SbKylsMtH3Od8valemztlNKsd1QU2OOgmCMaktF3VajAonnM5qlMjO7pCqrKjXkab2TGcLGt9QVhU7ewdEH7LgWuzYxjYtpWX5YwzdAhpJp1op1zMNcYMQvCeElMqmbZhOO3BjdlmNJks/xPQ6EfCSRKTwKdopCj6AD0LQghADUQPeg7WKFYf6jZFhTc557TbO1gRNhE0kic8Za3J4Jd2in8ZCJpVMjvRqTzrzUYoxQlWV1D5sQp6vCc5YjFjqekmjHikKSq9gHZeLSzDKoKz4/vvvYazj7qtvMV8u+OSjHxD9isJYhq6gDIFX7hxyOVtxNp0TVLlhC+6uwIwtR2+/jSFyfv6UYvW3eH34hPPlz/LUvEPIJxWJUDZMGRO8UCzPsbJgOBgyPT+m2j2kXlVcHB+zXNdcLubcvL3Howf3mYzGOGP5yr3X2bsrrHePOPnutynaUyLTughp4/OjEYubR1zMZkwfPOabH9ynGBSMhgXT83N+6zdXNBIoLpbExQVNcYpU++wdjSHcY710zKaeUAfQQAgeDSvElOCGYMoUNaCbzdWqEE3oTmS/SGz8nWdzua8al88bo+372j+0M3pbYqAwFmNLUJcNl0jmxyFGjApO2hMA2Yj22Y2WVVSh8RHTeBxFcsBjuvCgig8hV09IEcSgaIjE2uOK5CCGLcNy48A/6wo9H0SsKpu52jqDV4iabVNoQ1q1hFjnMG5RBteFZCjmrXc7ZGX7rp5hZ9rH0709v0l3+0y376cDh7J0iLG0p+OdDhfaOd/bA0JTyEaKBtZ0nU2IsF5Takwn9ZlkjwLBpxNiK0IUSWs76cRL2ihZSeSKaa2WbtxJnieb/hGB1j+NmwHeGSnPGrDpE9P+2rko3dvyZ1/zNOyIofbaJWsZkHQMoK1ukq8tn9BvX/Tz6QlsdbJ0jkD2BTFZiwGkI6qSM5HCrY0kzQsk7XPtPN7oa+T2UaVuGnyoGQyGFC7pwwXvaZqawgrS2VhgLJRViTEuRdyKEHKar+Q20JidojYapb1DMWnvvjKWW+N0M5M3aRCb1NfWINV2bnalb68TOUq5XUeUjri56hDl/pIugH5rLD57TZt76zp0yw40xuFsibTkVusQ5uhbjSGny+T3t6SNyVHmpEMtMVdTgYyYVLEleJSNTYUBidmeDB4JyR4SEYyaVF442yiW7b7aWjfztXTtv7W36TP/b8j/9Jr0mG7N0evfE1/0ie1e1pG8erW9utdtfcaLiB/RNPeqstjadbRz5kPuNxvzXJMc7SbJXk2VhGRrmU/lnwmKKwSx2fb1yroJyeEn2YSbSCFBcoRUu4ZGbTCZNtbcb9u39+yobPVSXkSaaW6AbSHiZz9ju+WuECrXhG597MilzTMi2493G1y3Dr/IZ33RY5LHsGqyM0S3H5fWA8OKTfpEmCTxQVqjROhEikUMpii6edweJDdRWXjPxHmMLXCuoJEa0Fxy3BJUCSF2tpMzhmjKdK4l7WfRyXdsRyZvom22KOR2mXlBv10VstmO1bn+eWhE0tqVTryZz5dpRTclr73xOr/08/8VRqMhb7/zBkd7u8wvkyhxVJitPTIoGA4rqErWLpGey8sL1k3kw48+ZTKouP0z7/L0O57V6VNMioxIfqRXNCjRKG4yYmdvh7MHj7mYLTgfjfmZn/9jvB4jX/8TP8MvvHubWzs72CbgV3muWpMO5K0lqrCsBWsHrKt9fnCpHIeCZVlRI3gf8KtAaJbJr4/p8FYihMWSZjrDFFUm7jKJ2za3SNt9tIf2sj3At/qo3UVizgQKMWIlZiKmnRDxOT2rbXwu0kaBWlKZqio4VjWINVTWMJ97iFAVBcOq4lIjjdekSZI3rBg9eFKkhCgxZEKiXcg0ids2GlJCTVBCa6DlsEaMYmwKqQ+NR3zDsCwYBNjRyNDAyBpGGApbpA1S06br8USNrI2yiJ61Tzo0iCVKoAk1LgguWEJoiFHxNBhxOLWZYdMskKSUPhIaIRQFuJhOSAoLNjH5nhTp46xJjosRIEX+mGzkR6P5/kNyfnO4ZWzyQl+Yqye31wBp05WMoib10bxJC8SgEKazpJg+Ho+oqhEXF0rdeESEED3BB/za5xDARGakw4TsVJmkIZT2KclBn5qchxxt05qvxpo0LmIqB2eDUipUAmNrGFvDyNiUytUOZONoxLBGqUODxyLOYqKHRohFJPhALAIhhDSB1OeN2eSxGGkk5cKa4NGyQIPHWoc6S8yRHVEMxhlCZ5YmVj/SbpogOVJHVEBTTqP3DSKGsirSNVwzJV5VAwZVxfzyEnEF0gQG1ZgmepaLOUe7B0zPzwjrOa+8+S7T5YqTk8ecHB8TEAauxETlYG9MMIaT03NCTObuk8UCF57w9ug1vv/73+GVd9/mrVuWfXnIhIb96nv8tacVD+OriKazjJg32oWZ8KCG4I8Z1Z7RaMTJ8RPKomC9XPLwyWNeffWQ06en3L51i1E1ZLhqGMwbQmhwPmCNZa2K04DT1A8CNM5yfvuIWfTIquGD712yjoGvvfUK0ydn/MLukDdHjr/8/UdUIrxeOB6pZ7Y85uL0e0gxZvfW17mxexM/tyymntV6Tow1MdY0zQqREiMl1qYUItR2leUwLzYSrwXZidks3PnxK5EYW46QbL/umRSXzhjKJ6GaDBTnDEVRpHlIMhFiyARujGiOJzU2aWyZ7LC6rP2V8rWhiZGwWFEWgarK240IXg2+8RgJGGsIIUW5mfZU2SgQ04mzCqKmc66ez6nfbHhXGZB2U3xxE7bvfcan3LRd185fEBQ2Mob5ap7xcdNj0hEomxPr1rnV7jVxK02g3ScHRUHlkmCmaGpW0dSXGjRzGrnSSSuO2f4EIBKjUDdJb6EqK8Q4sNnR93UXSZhCZJKRUhWOHFOJAjFE7JW0tmcdP7IW1KZHbOtEbTkvRjZu8cZx3hr1uu2It87YD98lPyzs1hpt2jGT9+kuYkOhza+VrRD5LhYukxJpk9uao7KZpZDapSoMq3IT1t8egEQNYMgh35mYy4cDToTSWmKWykgndZrzDC2mcGAcwQdWTU0MPs3v9joUjJqcHr3tvmvX9tvtv/H3N47FZqlqR0K7p9P9nRC7qJbtdJc2xeu6ncRtXOVNcypLNz47txly37aXLa2dkttje+I+vw6nL0opTra7r3Zut8t00myM2cazeRxtRQiqpBT81nmPSoiBItrNoVgmFdrrTNdoCBFCaBBjKGyqchTJC3U7XLdzGvPPjlRrHY2tu+zGaSZNtyN10mqlW5/32QodPw62luzPfEEaQlsb3nMv0ec+o+t9Y7FltRUIlg9MhZSeH5XgfYrM3ho7xqSDvPbQzsTclpLt07yvQX5/SHS3bcdg69wprENK/XZGnhEXDiQRCnslaK0lMyRrYrRTSGnLUXdWwFYjcvUxSNcom2e6uf5FhG1k4kjkapTy9h7XrSuf3ZXPEDbbUbUkWQtRmhjSfqlsDhTy3hFiJDQeaxrECZqLyEgmN2NUrE3ta9uCJlkDJ0SlWQeWuqIc2eTXkg61CmdwZUEQYb1qEO8pjGzmMslBT2twTnl9Zj5t4veuzlPVTbYJYp+1hK70aXpue+29JqgwLEb4JhBQJsMB1hhWswUnD075+L3H/Nwv/iynx3OOPz2hrEoObhywmq948Okj9m8cEtaBxgd0YKhGFQNncQiNWM7nM0bvvs5B9bN88Bu/Bc2SWANBIKcMShSa6ZzZ01Ocdbz71l2+8u5tfuUbfxZ9ckZ5PKX6q38PezHHXi4ZrpvskII6IQwKws6Qcn+X3Vdu89a9W6ze2ePJQvju/Snf/GTOEx0QiyLvqZsVsbVm1BqCsYl72Oq17Q5oSbnNXDRd35otYrJdBzQvPtEHPDnaThSCh9h8Zpd8TiFixSRZboLA1KfHxqVjuQrEECmtY1CWWXDWUACeSMWQGCJNVJqsMRNDpDIGR8CHHF2iijVKJOTwo5RvmFoogFeMj+wWA/YGJW9OdrgzLrnZKHuNx4rgYqDwiSwygHqPIqhEgpGcjuXwxjAzcOkjZ9Gz0JBOKZqkLN9ISJtjDETj0iAyObSpNTRVcL5BC4t3jiIUiE0l34JGrBFKk/NVnUXwNE2DKwrA5vUq51gKSFCCT4KvoimN6VqhaS9WAfJiF6Iw98kZG5XC5TKlGFVVQVkNSeG7ER+BxjKsqpTnF5KR2mqOtGlgITOU7dCOuR59KwYXYqqOpSFQOEvha+4Vhld3JtyNhn0fGatQqmI0n9zHxLpqzhFUqwQb8cayUOWiCVwGuHDC2lqCbQguEWTqA8ZYYnSIyU5HdvxikHSiHHwKV7UOKV0qESdJX4ka1FqwFieSiSCbytEpaZJJIsJiTKltPoZcFS2dXC8Xs+vtRlXssCAsBPWRcZHSVVZNzeHuPk0TOD074ZV7r+KDsq4veXT/AYu6oRJHocrBpGRYOe5/8iiJAivYoDSkf/V8RrE35Ozph/z80SnrZc3Ng4qoDb8iv8dvPC54Knc7U8Fkh2wmIx74XY6mp9TeM6h2YbVgp1F+Ye8Gt9jBjyYc7dxkYEtgTrmcs/v6OyyiI4yfMlvNWNRrNAYKElE2PzpkPhxSaxIIXq09hwdj6sWCxdrzj/+ZX+bnXzvn7/1bCx6vPKVX7paO75yfsJxPWTennJ98ymDngIMbb3P4ytusV/usLleslivq4FFtUK3xYQFmAHaIMyViLAb72VbFjwzpjOvNI1e/Y9sxbJ0r6azW9hXbDkk2wjryJm0UrYK/5vkfSNpkXtOLCkBNe5IuSXhdNpXikkMak1Og2ulVIYZaDas6ID5QG59SNLKjKsZgSpeiGHPZyxjYlEXceIbPuQ/t9UvHfGwMmvYer56mbt6z/ffGyv8iXAzQZxyj9v/nyagNut6Sq6ls28ZZS3CjGyOtcJayrJLx3RnvMZPoJGeiTccwKVqVfHhgQ0in9BZQIcbk/BmX1q91vUrrZXsF2l6nICGCpDUt9a/Nhw9seclt4tr2+WG+PpQ29P+50++u9dK7om4+qxsJW+PZiO3a67rQhsN36VFsTny3hihKW/EhR+Bwdext5uL2kNs4uInwEEoxFG3UDMmuCCFC2DjOjfc4W6Qwf02EQipHmzX1NBNnkdTGTcCUFp8rWtDOj26RSURFaALBR0QEW2w0jl5IBnfEW/dIuputx9s3KVuEahstKYK0Z4zSzornHcvrxvaKeHVmba0R8kwEFdu0xXa/6pXP3Y4Cy4FsyeBGr56mS6sdlfo52Ugxlxpu2z2vfK2dawT1gWgihiSa67PoqeRCEO3VWJvKCiddj9zG2t7z9l1v7wmbNXTjmjyzTnX3tzEYt0c3L/j9+qFXfzVb8/K5GtZb19SuSdsPtB+Tx6y1BlfaRJCyWb/bVKPkhLWRMV0yLhHwmqPGaQuItCseeA1YTVEJmsVq210tzaW8ZkeFVYOPitqCqkxVbdjqke2N4Nn9pX3NNi/8TIttPmfr8dYJZUv/JJFAZBGYf7Bo970uokRfZGK9eB6mZ4SIwauhCRGNIdXikER7udxAbTqisZLImlYHKjtyPkRCbMB7YluOPUchaysoHoXoG5rap32U1teJ6YDWCKZyUBZ4JB+qZEcxxhzZs0nXpFM7+cOwHeu4bQZt7TFbr71OTHZ2+GN/7JfAR77/3e9x694d9iYTLi8uaQzc3tvjkw8/YjgsODrco14v+OTDY+arNYNhyWgYObi5x/e+9S0efvAoSYO4AePJTXYmI5bzcz587wNevXOPDy8Ddw5vs1MWjMoBsZ6h4nDViMlol9de2eXWz9xmcHaJ/Pa3Me9/yuDkksrn/ogBok/HSjGR6UqOHDcQxRHEUQ8KBrcO2Pvqm7z9xqv86dcmvHcCv/t4yYfLgpNoWbaauFk8HDGdsFS7tm5G4/ZE3ezlab1O+0vKMkkSMGQyzuCh9qj3ScM31IT1Aj+7oDk//sw++ZyRNkJtNiviNIt7DpyyWCfRWOccxXBIGmgh5fJJilzwCJ7kHIpGMAEfk9BvHZq0oZFYbE+kAAgxsVAqOGAsws1hyZ39V7hrC45siZuvKZoGmgb1MXVc6zBoyi2MmkK8bA4hNeKYONh1cNcNWLuKWWGY+4bLJlDrkBCHRLGIBNasusmeuitSo7kBFecsrkh6AYGIMQ4rgh0U1NbijEsl4mJEjEnEjcnhZ5LCwbymUL2om2MSS8sqXxOkO4QB2tQWYeEDtVeGleX8siY0gbKoGFbD5Gcp3emeMZYQEnstIsToM1EBgSzKmwcmMUUnxbZqVnZCbAyMRPnFWzf42rDi1XLMsFakbtCmJjYp/DAJo2aVf+9TOLFIimYy4Iwwso6b1oEpqcuS6arhSeNZRCUMA8FYgljqpp14mdVMK3InyFy4AldYTFlgEExUCmsYjAZItBgKlk3EiMGYlPplrWRh5Uwo5ZNSY5KBZoxBxTJbXi9p0/iGh8eP0jgeDhCxzMOasG5wRckn999jNKjAVlxML3j68FOadU0hllJgWBjuvP4qi/mCEJ8QgbquGSmMbMEa5UlouGjgTx1dEtaPGQ8dVZVCQV+7ueYfDt/iN56WXMSbtISCiDIksi8ld2LFjfMLxjqlrD2joAxw2ItVqh41vY8KFAilCHo6Zb8csHfvNXRYMauXPDw+Zn1+zqJynO1PKIcV9dwzX3qGo4pRVfHw0QV7r7zGf3wy4q/M9ln8yisUv/stOD/nfL1ivlpijTAsCoJG1pfHPDx/ih39Hns332Ry+DYTbrKcwWI2pVnPCbEh6gINa9Q4rB0S7OA5Z/PHQUuEXHXsn3cYNo9s3JBNzn3rVW6uS7sj4i0jR0lVwGwin5NjrDl9MWCIubpb3JRKzJtdkKSY7zQ7OGlap7lOcqBDSAaN1SY5i7ENO07Gb+PzvJFkJBpCOxWz25vvc8vK3DiFLzLQczvo9iPSbZobM1m6DbZzKr9IV2PLKXpR/BBsnu9M6JhpEblqfG+7/tvulXFCUVRILsNp2DhySkwpfzGVlDWtPpwk8zZ9trY9l/vcY9TgNWnbyJXv34y92gcIHmMt1hVpbUO35kR7mipX/1deZImTomC3ulahiwLdOs8XNu/fmEfmBWPix0Hb+NqyKt0ISoRHS2jkR7bCMLrxtR0Sq5tnufJz8zVtBYuNCa9b1RM3aW0xz3WNKUU7SEwpW1k8NOS0ADUGHzxWXfd9WwH3aeQLnVC1D4k8MLotFN7Ou+wed5cvW2N7a01Sus9FryYeplduUoautEHrnH0RUzFfZ9tFbZxNwsZR2jbBt9mnbWd284Gb36+6zfmRLHKag8i670Vz+mhUoo+JWDGSo3TTvDS5AEQbJUcE7yPW+E5kGFIEc2kLgipN8GnmZwHWlvq7UrZZNzsGW/3VPd3eg7C56K0ZdpVAv7pmPT++viBsz/F20LRshWxf75bjRE6j2v6QK/edWsU6Rwx6lbNqh0MbbdHuT0qu/qJpv2yZoeDzIXDSDAtBaNSmPdQIIpsiFQKdiLF1SSpTI1gbMXbTP61mRtoVzeaWdfOaNoKL3BTb97ddfazt8C2a9DPaWLZNiGvH1VS2Z8bQs6Lf7dqwtb9viLh2329X5rxyaiDGhiT+qxANYkGtQMzWqbTLuGZfM9lAKgH1DWVhU4ROcNhqgDiHxogjp6pGjwSPrtfpeo0hBNDGZzJVk76VCho0F1nIl2wSgZMKBPxhB/GyFVS3iUxt2+EfJHZ2Jvyz/91/hqcf3Ses18Sy4t7rr3F4cs7vfuv3mFUL3njndVaXZzz59CPqxrNcNIQYWc+Epw8+ZbS3x/6o4s7+mMvTM4Y37/LLf+Yfo/I1733zbzI9aXjwgx+gd2/wx//pf5JbVcHidMbZp8eE9ZS7b97kZ77yCrunl8hf+/tU733KaLbEUqdiNa1HK6QMD0npwK2eic2RilYMZRSqJYxOT1l/533WwwG8fotf+tmv8bXX7vFRbfi7x4HvTA0n0bHOVpwY6fSO0nflrJvOZsv9o/m/TQgfaLK2VAMaGrSu8asl9WqKX8wJizl+PmO9mOPrNRpS9stn4XOnR5mYNFiCGJZrIQQYFQWX61QGyxWWyXgHT3LuPYkkGRRV0vFsklBbOnkLXXnkdqEipsmVUoUE0YjDMlHhlWHBm8MBNwLs+AYzX2PDLEXixCRmnFhvS0AxJmACGFOkEyyfN1XxGBNzFEZamEtZsmeEPVdws1oxX645HdecF46lKHWMqSS45pLcIVCHBmxS87dAWQ0oXIEPnrX3GGM5unmLndGYQelQUVazObhUlqw0JePRDsOdCeIMVlIKVqpepimq58op8jWg3b90M8Ahsg6wXsOoEo6bJBZcDAzDyZgQ08lARNFCEGOpg08l3EVQDQTdCPlqDhmN7UaatWBUlVKVXSu8s7fDV2zJUR0oVw0yn0OIBJ/TmDS01npn3YkxaFOn6/dpnIh1YALGNiA1xcpwIMLeZclyZ8jZ/pgHgwHTEFkEn1I7QurLJnhiaCAG1usGjOlKgPt1AyFSDUtG+3uUrsLZgvW6RoxSlg5bWMbjEZPdXQaDEc4VlNYSgs8bsyMY4fJyyZP76+vrQ1JEj/eBO0eHvP7K67z/8BNWJxfsDse898GHLFYNo50jHnz6EGM8s+klgjAwhlLg7t1DxJU8fvgx6xCSVJCzECLDwQAmQ55WjqPxBff2TjAIo8olg0BhUAlv3l4yC7/L3zz5E3h2OXCBA7vgzvyYg9MzTLNORkhIOlRBDGsiJQZLDk3VgNd0YmFECGKIxuKdQauSV3cPGLz6BsvJkHG95MlqxiezFcbAwBnq5YoQlSdPnmCqAYdHNymdY/TzP8fF++/zye9+kyAR6xyGpLsijaQUtvWC0/u/z/TkQ27cfoW37r3N6ewWs8WY5fmCxWqO1wZCQ4w1hAVZLfCasWV9vcBy2jaj0c1J9rMhw89YuFc/RCBpSNiUlpQN9a5Kl7RGYusyg6hiW92iTKCkKjQpZdPnua4ohET+RElErkTNRkqOxGsCQUOKQjS5POr2sVobddM5QpuoonY/eN7qvOpMXXVArr7kastcv3Wq25s0mUBq16/u3p5P63qu1zoj+3m0ETlWoHBmc5+tg0hIjqZu6z4k3TgTASxeUi/bQNpxDcnRj65zDpW8/6Ap8lRT5UW1Fh+EUixJxF07Qb42crEdq61o6rNjNG6JeXeVBlvXT7Y0N55tmK1OTLb7hoq7HmzIqmfd0c3ca8fYJpooXatu/P52c33m0565hS5SxrfpL9kwV9VNel12pPPZQqc/FVGskVTmFsn66IqJAd80WJecSGOE4NMcS3ZJmu8iqcqGSaIdGGO5Kiq7fV+5FbbWpVZvS7LF2jpULc3Wpta1bdTOhY0do93nfSEEakfc5u+UbTJxa35dIWVaF/DqY1eJHbqufdYmizEmwVlozfzu9Wl9TOtsjs2gjbbYzDNyBbG0pnptcvqa4rMOJALWtkSnZI0OOuHpkLUF0zA12XnZJmDacXnlCvPBQY6Ee3bitevIM7MhhSF8tmPxY6NtN54ZI7q9el91/nmm755b+K9+PKKG4EPXFon82sjhb4vWKm1kqWb5ybZiW9ZxymLS0QdCE7rUm/SJEWvTgXGn8Yhk8tt2lfeCtnVjt/cA7Qj5bfKt3evayKBNz3L1nrcXZb3aPpsVKh+ZuOvXettGu91f2X+79bJ9webXq/jsdcIgOZ2t6fqhUwLNjRJjTBqcIXaCwhIjNgZKB7s7JePJMIn0m4rCFEl8N4l9pT2wEEw1ZDGoUQkEgSZEVr5hWTf4JqDGkFXKUmSPafWPJG8hssXty9U9rN3Ot5/faoj28bbfro6HP6yVfjQ0jed3v/U+6+mcV99+m9o3YIS3vvEuxsBv/MZf51snx4yGjkGRsg9YN4Smpslj98HpKcejEW8cHVA1FpkvmX/6Kcen50xPzrCs2R96dt+9x6xpUCl47Wvv8Au/8nMMVxfIRw+wf+VbVO/dZ3gxpQgNqEcJV9P8Ynvgk/uX9pCxfTR288UoVChu7il/b07z/n3K12/z9Z95l7uvvM7bO2N+5zjwg6XjQoRcry+rD7d75XZr68bOi/n7NVedrVfUixn17AI/n+FnM9bzGfV6ifqUYm1EwFlsVWCdxRnl4jP65POnRzVJINhIROokSjko4GTlqWOgEGE0nKTKSiEZe0TBGkcIDeRoDBXFx82iI0oiBjLxIjFt/mNnuaGGd6zjLsrOfI1rArH2RPW53LSg2iAhTRAvbeekU542xD+q70IZQ/SosXRl9kw6BYnrmlivYHGBTiY8bmqO6yVPLi9ZhQZxyelVlVzSO1+rITkiWJBUISqK4L773S6ixposbGyV0hUUxmGLitFkh2owpBqWlNUgRZJIZG8yQbziG/95uukPhcmM5HaalCLM18q4FBZNYLX2DCfCeGeP1pCPefMqywKJeSFsB6turbk5hFtyXypJL2AnwttFwbtlyc1gcPNliqyJiRU1eYvrjNjWMGh/FwM2VYRS8tyRduNJ5dFNDgXXesHq6SVhPeGDdcNiveaT02Mu6zUYwRU2OTgm6xdIMqKddV1/tpt3ylNOnqNKJoucSSU6xVIWA5wrsdbiijKfZCpFUREDfPrRxzx9cL0lvyPKwBXEGj56+CkXp6fcHO/w8OFT5henHOwfcPzkmJu393nw0QfUwTO2JSXwyq19RruHPH70hIvLS0yI7B7eYLizy/TijPLwCCkK/HrKn/zKgqANg0kFrj2pTToKo4HhZ24usGffZX12wCTMkhD4eomvQ3baU596Veo23DifOKQoJLo835wajpBE/OpmTj2fsz47oxgO+bnDQ+LuLQ7vFny4vGAVGuoQWTQN9WrNx+9/j+Vixs2jO9SF5cPzE+ah6Uobk40mcWldkvbkul4xO36Pyb1PePe1CZ/O3ubR3ptcTA+ZXyxZLRf4mMgbvWbS5vkTzA00W2BtnIVsv6s1dl6wS29H5m37SUHJwtva2Uq6ZZSLGFKlteTQt3nbVjUtGm1RixjyNQixidhCsTmcPkQoTNIuSae+aZ44axFiygmWJGgctwzS7RZJm9+2YdemWj1vnl81dHIrdQtbdirbVt46dbxu6NY1b1K5tg3Uq67Gdq9Lti6vuCOyedWVqxVSWwqJcM7C0a2YKXk/FCMpkS3mz85rkhWDzSexMSaSoPEN1qe1yziDD5rEG22RD5M9gs0lctNnJB8gOypsjH+eaetn++mZW8mnxJ3b0DVOu48k4m5LF23rs68dcvWXDS0kV67/qsNPa6+xbUp3jv6WsyRZC6eLdBWyWH6yQtr1smVBBNAQMSZinN1ED2Sj00RFrCFaSYUNNFe+NIJxDoKmKBpRBlVB4yPrdZPKTltBYutQJO2GNt4N5ErKx+ae87i5cqfSjYGYSbfWaeo8pm4d20SCtOl3X0xXbiektGtB+n17Xm3dWcazc/Vqelyb/rRZNzefF9vSzvkl1liMSQUMxIEUAlayrmFy7jWGruQ3rqCajPHrdd4jIyFkZ94njaOgMUXYSBon1kmqwrd9C6IphU7aSLHniZitUb21wmxSEDdDdrOGbl78LOX9Bc3F5yYcz0Rr5Nc8s84837/PQzQdJBgR6rre6u0csaQ56i1EovdYq4hLlb8064EJQjRt5GhLYEeaEIl1AyQ7MjnrgkiqZhN9Q4wxObeSIhVDXkeD5nRFtkfli+9l25l/hou58nzbXnrFVtieve3/+lzP/vjojPOr17f193Z3dfxwuydku1z16izdDEbT7e0RpQ6eSCJH08FSOugVzXtjiBjvqaxhUDh2R0MORkMm5ZBqBW6muHXArRbYxmO8R6JP2mWiSc3CVXiBWBrCoCQMK5pRRb0zpnGWuV+zWK1oQsTYIpUO32qGNm1ys65s75d5LLdzb2s/aufk1dG90cLbbt7rxGq54uL0CaUtcFXBYDJO4vbDAe98/atcnp/x+9/5NoPRLrX37I5G+OYspWVY4fU3X8NgmC1rquDx8yW6fMCHJ5c4V+LFsxwVzBpl8Z33MGHAa+9+hUFYUnz8GPN3v0f1nY8Ynl1ShBrBdwV8nhvPsv3r1j4at4ZeZ5elSrFeUoXYSfDw4ac0j8+oXvmYyVff4c6Ne/zWeck3FyseZa2k0K6e+UAlEegKpChkUPA1YbmgmV2yvjxnPZ3SLOf41RoNPl2gtRhX4KoSZwVnI6XxVLKi1Aannoef0Sefr3oUEBxYsUgInUL+aAgfT3OUhCi7kx0cEFqlVhMIhWEVIuJjqlBistGIIaokxx1Nxkc2bvYNfG045M0oHHhPsUwpUEEgNiEJg8YcGCpZ5DDnsgkxif8qkM71yWtAcjbyKZ9mz99oFgtGOSfy0WpObZX3zs4wZYGIsFotaaLHmiJFfRhNgp3ZSBdj0Nh0Icjk76pjzrFsjRcLRl0u92eIT5+ApmgE307oGChIJ+NNc70Of4gpP97lik6JMxEWdWQ0hNor68YjAnt7kxSCa2ra5IJUzi6x1llzLZVJjmkAt2WTWwLGErlVVfxyNeLeqqFa+5TKFlK6WBL3TTpHabzkSmCts5hPkFBJmhsiSbiPiEaPiiWKzdFZieSpTeRv+wV73vDw7BIfa8aDJNw3XS1Ye0Gs6TZJNcmxicHT5kwnvZ4syKra+YFigJBCmi3Caj5NJ1wxEqJPwqskg9mSRJT9NatJCzCpxqyahidPH3L34IDRaIePPvhtbt25yWyx4vbdAx49/JRl3VDaEqKyM6lQY/n000c8evgEW1bs7t1hZ38PnGMZGk5nF3iv/Omvrxm4GcOqoCyKHP2VjnxNFFZPR6y+5zh8OGfVzGgkUrYbcHsClI0GCSHl/mZxN4UkBpiraIjIVpUum0hfsgFa19SN53I2p6wG/PEbR/zSrTd53y/4W4/uE01qZ1/XPPjBDyibBquRJw/upz7OjnvURDYZSX1tJIlCxrrh5KTmv/gbnp/9auC/9kt/m1+Iv8tH56/z8eVXOF/eYnY6Z7qYXfvGuDE2n3ckNroPetV40auve9GntiHd3aukNRpC95kKLRObnovJibR4rNgutSb1VdJ+MrmKQIzJqViva7CGIK6bKyXCaFiwaiK+CcmodTmR1KYS6m0UXnzGddjoLMDmHKq15uh2XdPqR+Tm6U7xdNtx3rTH5pEvwrShIx67P9rLveIs0ZHY235QS1Y8m04Vta3ORHbYk1NsNI1hHwKV0FX5at2zGNNerErS6MqWsWgS1XQpJj8dDkRF60CUOrVSyKnFRqiKlAq8VnDOIrRVoFLEmmrIpGzOm20vvvvZRs5w5QmBjuzf8gU7I317PLTtdiXtA66k81wnXphGIHTkErntP9u9ueLhprd3jlOyB7q0GISmCcQslm8yYWPy2mecQQqbqtHkE3MhEmoP1ATrKAdDXFXQ1BBimlnBp/2qTf1Qkg4bgCssRbnRFUptm1J7jNkeg3L1dnIjaN4bkdapyq5DNzd163do05G6sb7d4V8QImHrHp7xFNuFokN7gxs9I3ieTO7aRTQP9c0YUKSrHhMywdxGumhMQqYuaDqwjDHp3bFJfY9NoDQWmoaBtRTDQTKDIGkHep+IMlfQaMhzXHHW5TVCu9LtbWrMpsras/Ovo8vS2pSJ9U3EYw771w1BKNK2z7PNZriabP/F4IWR5lf2QH3uYdl6ZvP2TdpUWq8D6/XiyrKV0vHz8zZXiTGt05zGRYyBIIqxqfJe6Uw+X8wl7YOmCLdIFqBNJdl9bjOxFsSlSH9t9zHt9N7aiDvY6r5nl7ut294Eq764H67sQdvE1xaZ+cWQp3q1m+Dq92jbF/k69EpP5Ci05+/hCvJESaXaU9Qiql27tbFDNgZ2rHB3r+Lm4Q1GMqK8VIYPlwwWJ7j5HLNcYlZrTFMjvsHEBrKWFJDSrcSBNcTCooMKHQ7Q0YgwHuFHY5bjIeudEcu9irlvWDUNIanOXjVU2sboOri1iLbWq25L3bYYro73TTvINu9zbdDoWc0vKCZ7FG7IeDzi+MmCxapmfzziaz//c5hRwToERoMhZ0+fcnES8DGCdQx29tgfTyjOpsiqZj2YYtwARjvYnQk3dwvquGb18BwYcftoxD/y1i77779H+TvfZfzwmKJeIRoQfHevgm5lmZkrRJfkQaEE2iomSRw8p00BrnBIYbGNR3zAZK0xt1pRfXCf4ZMTRl95jdvf+AavW8tvfFzzsTZMQ1pjVUM6NPMN2qwJy3mKoJldUs+nhLrBe9/5jcZCMSgobYU1iiNgTUPFikI9hTbY4DGxQaLv9usX4XOSNoKqoSFpiSw1sm4ig9LQLCO+9hhj2N2dUAo0mnKzLJYCSwzJ0EMlh3Em0VbBplPBXIbIoNwsHT87GvCmj0yWNSaCNh4v4PFgAjaHHkrryOfqLiYEVD3R5A42LvkmxpKSolL5RAkxOZXGpBMiVXDgnXC+9twqC6rCcVqv0aiMB0Ma75mu14lwIAmaiSbCKBFDbcncZJCaXIo8KjTOAIHCWxrvU5RHt2jmkyhJ4sVOUoizyhchfpqqOvhUxypt1N4wXym39l0SJl6sOtJGTBIiNSbl4RKhIaRTHiRXRkiGSwr/TKykRmWI8O5oxC9VQ/aXNc57pEltp21VJWNSvr4IbT6g5Mc0pM3MGHL5Z9vlkKYJmsgSjSYZa1mUMVqHi5G9qLw7KPnW5ZxGPYWz7O/sMl8sqNc1alIEAjnqo6Z1kDSFv+aFM2xdp2RDNYXSZnLHuly+NXaGn7WJsIkiaaO+Rggp/XC6OMfPp4TRmN/57m+zPx6wXgd2diacnpwwPb/EZkHwoRNs6ZhOlyzqmjpETGGYqzIqSowq8+WKGAL/0C/e4u2jbyMKReWSY0g69fYXjovfG3DxoWG9bJK2Set05M3FxmQER0lzS2JMERoxdIVINEiOlvJEDCETOFEsIVdDkayhJZLKDddhQXjUUJyf8ws3b/Laa+/yXz69zweX50k3RQ3z01PmFxeYGLBiUgEc1VxlJ0cl5Apn6hvqugFNqYFfu9UwWwmv37jkcPwtvnbzPT4+fZVv2q8w8Lc5e1xcax9mT54rZZK3HKY2CqM1sLbsz8/gHzaf1blK7YYWhTrWpOpTpJ8mZAcS2hx+mwrqEURolJSfHYXCCKVLr/OSSYUQsN7jSYR7SsEiaXoVmRy2rc8pSS8XIAuMm621rY3USGkX8RmDbttsyxouzzgTLxJr/AcLecFSna6/vcr4/+ftv740y9LzTuy3zTGfDR+R3pXJLG+7ulFAG3QDDWKAoUYccnGNqDUaLY60lnSltfSf6EYXuhktXWh4MaRIQTMEB0CDDaLRvsv7rMxKnxmRYT9zzDa62Puc74vIrCYbHTW7uzIiPnPOPtu+77Of93nbfgEfT1bDEGj6rHE0jrgfjXEfPQ4vJHVVhfXJxzNiKZmHwRogmtg3zjusMAilUUqHmPzoaEjjQNTYyrQObxPmmmoZTom9CQcsIbURwjfgSRMKML/GNUbq7DEOnxT69mfbbo9hdwhEy+hpsiwJKWkyaxx3mb+7YAa4tOw0EcaZb8N7mno3rpecsb7aS4nD3qSn3e+tCf3SGNy6nfphX9UirJ3ezcRtvXWkaYpDknqFsBphDDiJkxK8DpPMGYiM5qKq0ZEZgDcYZ+NI1GFtVWEvEzRsn0b7JfzXOgSiaffAlnZImtCuQzvc3CO3YyH+eniKHLdt0zBn481mNSCAZY1Yb6y38BEwF+360aybMzUs0V4vthBeNG5W8xkZQ8TD/BPeo31INFHVNU5bZOrDQZ+S9LOc9WGPjcVFFrtdcmtJqxo/LcDU4XZJgk8SqpUlbJ4zdp6d6ZTd0QGTaYk1NUqqCKbP1hSPDyGwTbu3TRzmUrOxiLZ/m2duA9xm/7bt6Ge/z1CPI/vWMZcje5yYe21+as3Wx6YaMyWuwxebv1IAR4vStDdpRaHFTLqiec+FE8kWPFYyzh0JiVBI78li3ysEygUQofaBtq1sHDONLRPXDydEYJ/66L8wg9WOTo35JeRLm9vTMj3bXjn0RY4AN49rn+Mrj2Z6nAcdmj6bZ5mKWbP7x3y2/f7svXY/jclbFGENUEDiLH0FS4t9TgwGrHUWGOxnpDsF8t42YusBaryNqkcoX0b7tAllnAcqo28S/Q6lFF5pRJJAkuLTHJd36A4XsWsrlCvLjIc9Jv0eB94zLsuZ9EPLBvJHmv3oWjibAKFNfJzOc68f//J5uAYerl+9x+Vnhwx6Kb1en12xx/7OiIXegIWVZS4nz3Dn9h1qb1nfWGN3ZxfjwWtN7SVep/SGC/hByii/QL1wAtVLSf0BbvoQXRQ8sXKWy89f4NWTC+i/+RWdd67RK6YoV4XTxBCKE/tBRrwrCPIjFE7qEBDlJU5p2tMBF0I/pQi4gsIiRGAnJtZFH1vglSM47kG7UY+nJO99Qrq1w+++9AxrT63y77cm/Gpzh/3SUlUF08kB5f4e9WSMMzXYqGGkQChF1tUkSpBITyYNqZuS+RLlKrSrEa4Odpl1OBukYox3WGupjw+0geDkO6R3SOeY1o40VRgPVRVidnu9HghJIgLiJrMg7FqWEZgRMqCP0oMNqdoafz3zjo0s4dluh1MV9IoaahsbVgSKm/cIHzNXCI8VDu1l8Am9QUiP8wqnNTpJ8EmKVyJkk3IBIXPjAoxDOBmeRYdTRek9Pes5ozsMKsfTUvNLUzD1wVnP0pRBp0MxGjMyQY8DGwaTbTYUGZxY8CgcxoT04dQixF7GcCQvZQydCgCNER6NBmdDZhehQ3MfKwzuQ+hCayzHDd/DuPRkOpj940mNEJ7FhSFKZVhbIJwjiQCZMTXG2JBBSYg2a1KT3jpxjkUBlzs9nlEpg3GFrEPMaZOZIBhRwXF3zDhsjSOCEGBMcEayDNIkpK2NDC/qGiYFGBORF+IpYAAYLiddVOF5VqbcQrAVN4EUx4nFIW5acXMyoZQ+gDeRBSKkDBmrrI/ZXT11WQbHCJBtVh3ZGn3eW6raIWVg8wjvqLEIERzaoBtwfEUgmdQl3lRcOHOWGzfusL29xdLKGmmqKYopD+7eQwJp1A9ZWxqQdfvsTisePthkXNesDIecWF1ha2eHoijZ3nrIqZMLPLV8E+Em9PohG5qTII1ifDVl933FdNtifT0zIKRoRZ6lj6kwZ52JdC6eukcjSATHPGhYBYPR+TC/nTBYIbFKhYwKKoQBWAIjy5ZBiLEqSpaGQ/5XZ87wi06Xtx8+oLCO0cEOha0xreBjM/Rtu0lKD94Z6qrCEU6g33hOsDo0qDShIQ+kaUE/vcX27V0OTBcojrUf5yrHzEmaVdpHXZjwOzOH8MtKyzGGYBw0UEB8zwTGkw+LJ9KHE1rvwxhVSgcAzcrIRARVVmhp0VqQSg2RNYN3aOsRddDE8C7Q+GsrmdQho5rUEqcUIVQ1ZFCIaQIDo7IBZKIjMM9WmTNXaMJnZpAHzOKBmBntc605a14x9+pXY5y2bmHsr3bNjreeqQW0HTv/g0YXZRYCFIxvf8jqDr964XFSMC0qlgjOhlaytVOECJon4aTfhWkGCKFwSYLUGd2ki5cSp4ORq3XQQHG+DidDIuxn3lrwNghl+sBOk20osJyvVgCk5ix0IcThfhF+di4/Z5A3gGHz7I/sd48xTJtgnmMv890mDtdlNiJDKvvDFWvG4lyQ2JxDGUZeHO+iibUPTArX0L0JbNvm97Iy5KUlzRIEIfNbJ+3Q6XbIEk1HK3pa0tOQK4HUCuM9FsG4dowrS0XQGrTWhoQMpkai2rU6SwARMrAIF2eXn+kTeXybkj08ZtNAIfRZRV0T3zpZs/ZoRsfjlU9mTJ3jL3EdnZs7Im4Eh4TfhWjX02aV8Y+bczRAeDPBwlxV8TohW0kwvKNsdHDqVfAzlBV0dMJat89ilrGuJOvOk93bgrffh62HiMkBoizQROc9HiJ5qcjSDN/ts7i0xJkTJ/Aba4zXVtiyju3KUEfg13uPJTB7mv6hfRQxh8IcnqOzcR3H52PAjkNMh2a9/WroGYdvM/f7o+9EJ/YR5lT87XEOcXR6hRB4ISirOowJ37SMoEnBblw4LdcqQXmP8pApQS9VDPOMYZ7Tz7t0sw6pDLaKiKwlLz2lKSnrgspZirpmVFZMakdNPBWRMeQ/MiVbu7dZ2Rp/4tAaOnvW2cthz5nnyIW5daR//GP+aMfEVzQVjyJv8yBMXGQeCbhr52izaoq5UXkoCBe8aE0dZ4KN2bDnF5Qi62acyjucTLosuQ7qRk117S7i7l2SahPFCCGm0ZGf2WCzXmg7YWbnxjYT8TBWSA3RVvWbd7FfpKj+EoMnLlBdPMtur8uOUowFwdfl0bYWQs699jjm07wNM9eujR3Bo919HKXT6/Liy88wLWsOxhPW19c4cXKNaWXwQpJ1Opi65NSJE9y8eYutvZIyX0V2TyDTjEqmOOdJUoXLuuQnz1NUnnR0l9zs08sylk5u8OTFDc5rj/qrt+i+/RmdukB4Q5OpccaekQivoh2hMGmHutvDLAyp+11sN8dmGV6rqJHrUFWNrmrUZII+OCAZj8mqEm1LVDw4C6HJKoyu2JCpl6i7D0kPfslzzz3F4MQZkvt3+MFHt9guLXU8ZNZaIfMErSDRkApHiiERhtRXKDNFmwphDcbH7MI24BDeBUasJbCXw5KgSJL0S/vkNxYidjJkFwkigp6i9vT7itJ7pnU4pev2u0HUyliEBK0AAgPD+xDDaT1oG6agcxbpILOG05nmlE5YlZq0LrFVEB1qheu8DyKKIn7XN/uHQGYJYnWFZH2VztpJ1PIq+eIispNDogKgYByuLCh3dzCbm9R37uM3N2FvjKwqnBN0dcJSqkksXFIZm7ridkTnpIPzaZfFtQGf7e1yx9TUKiiWGxMXWwnOh1g5KYK2hyFQzK2xSJ0EfRvnQboZWw7RRCuAcyEkyflHjdjfpjQrhguLT3AwAhthVHq0Cto7+5OQG2Yw7KNUirMBslBKB20M50IsbmQPhn70eGNJrWPVWS50Mk53c/JaIOoqxPx5h1dAy8CIITC0kRohy8yghz63QXb+JOmJVdTiCrLTR+gknExahytL3PYu9b1Nyi9uYG/fh9EYH8NjBjrBOk8qJZezPr4agxTkAi5qzanVVT6YjPig3GfqLcZFllQ8NRTQZr0ydU2e5dRlDTh0EugDzgdAIbCRRDQK4pIiZQzdCVoqx1oETKuCQT5gfzzl3oM7dLodJpMpSSLZvHsbLxwdlSGtYW2xR3cwoEZz/95NFheXuLR+gsHCIjt7+2zdv4/UmuGgz0sXRyR+nzzTqCRFCIkZCQ5+oRh9FlPSRwc6ZA6WgT3hCEZ8NEAQ0SaRCplnpFlOlmQgZdSuAlkbfFEiqhpnKrwxcUJHvQznccaAiuFsTfpbFxyU/Z0dsqLgjRMbrK+d4Yc3r3FrUuGjdorwAc9rfto47YTxWGOpfGBEPX1e8cqThs1twZnlmqClJJgWmh9/0GN34inrXUx9fKDNl8/qQyTZGZwzt9PPTKG5+Ggh5qyBeUAj/GVxUR8haIfZxrEUYT7XVU1WG6QKmfoyHD0t6fckvUyRJCHVtJRZ0DWJ4K+TilFlKGsTMpt4gzEej8LKpiYWLUN4jUJGbahQv5n/OzPN5v39xuf4tcugmBlyM4LH7PnnzNlfc5HfpgRH+xHjyh8JehKH3p192x8xXQ9ZdY8a7VJpijJk2JP4lmVqHdjagYkAilCoJKXT7dLvpHQVdHxN349ITIWmYXOEA60pHrIuRqdUMqPwGYUJoTfeBYajjqC4i1ooMK8hcuih2n5pAACQcSs/HArWhB40/Rb637WfEXO/NU1y3E7GDGeb3aPhUczGTpwwjW5SW4lwmCDmKe/R+ffNtUVM20xwGAXgTWSbzgEjSkswDoUkVZql4ZB+olkSniVnWHb75DsHiJ1d/N5ucPjr4Hw6EewgOj3kwiKsrGAWFtjvdNkWKSOXUTlF7RxeBt6XxEMjiBxr3AAqonGMm8dpHv9Qf8wq34KOMcQrZDhrGmMGs83m8vHPxyZ1+zzDSRDZ1E2PHslY0wiqzcaUbPt9PkjjEYfK097HRk2bAGoGA7yfZCwvLHCuP+CUh96tW8jrNxBbD9DlFDl3oi+adHpE9k/j0EuF3NuB+3fxVz/FpR2GiwsMT5zgzMYJdgddDrKckZSM66jrEen+oGZrIXN7yGx5aobpnAPf9uI8vNO2gmja69ABwfGW2QFFqFs4w3v8jZqQ2HAONC8SOjdfZ5+OrDGPdFCVVWT3hs9JKVFKRra4QzhLT6YMpGAlzzi5uMhKPiSrEtgu8Nf2sdub+PE0aKAQD5i1Js81cthHrS4i1ldhLWOqarYne2yNJoy8C0zkCADM+zKP6veE0kAKfu55ZnAGIObG63yHzQ3h9rKPAEL/kU75e5VZmPZRHTAOPeP8SIMG6hX/kUo1bBXvwAYdCqSQZHie6Oec6p9gsZT4T7fg+jXE7n26Zg/JGER96BCs0fsLQ6GNgWDe2pKeYIt6D9bgXR1rKfFSIrUOQrLlCL//APXxR6xcusjCxQvsL/TZUYJCEFmxcq4fZ6LDX8bWPRSCHJ++qf7syON4O9GawJ4e7e3yxc37dHodTq2uMa0OcNLTGwxCiJD1DBcWubNf409sIGRKphTO7mNMhRYW7/fJJh+zVNZ0lKHX67C2scJT505ycmub9O8+oHv9DqmZgq/DTimChEhYowRWaEzSpV7oU6wsU586gdlYpl7s4fodXJ5CoqMvHfdjF2RZmJbogwnJ7pj0wUPSB5tk2ztkByOyukYJSxO6DGEuKgTd0YTk3Q94cjImvXCKrjjN33zxkAd1iRCeRBRoLNrXaBzSVAhT4WyFMYbSVExjAoAGUG+zDQJChAMZJSRSRy6W/fJ98TcUIiZu7ALhJQWOce1YSzSVgGkRqPfdbg8vFZYakGRpFoAZEcKSShuELJ0PWVwchm5tOI1gWWb0S4sY7QAh1AkRxYslbbiVNwavBD7rkpxYRV86T/7ERfKNE2SLiyRpClrjZYKSERRwrkXUB4TfTVViDvapbt1h8sFHFFc/R+zsk7oAQC3ojGd6EmVG1FLQdZKzacba2jpLGyf55Z3b3C3HeDxZBKSkUhCNWu/DQpIlIbtUMZ3i0zRoRliLx2JxCCFJG7lFH1JIaymx4vGL99+3NKaTUHFxF4APIoajCrQQpFqwVxic9fSGOTJJoBIgBUmaBhqwDai2cwFA8YTTvMQYluqa06lmGUlnZxKo99BulvhAWcPRtg9aIpaHpFcu0nnmSdLTp9ELK4ioHyQaIeIApYAP6dbl2QvwEnhnMPsjqtt3mH70CdXn15Hb21BXICVPDxdIS82eq8nwnOzk9M+c4lmRIm5e5YO9hxTO4ZVEetBKQAQXvQdTluR5SqIkEkead6IAaEQQnQypeyMjy0X6uY/govR/D1LbrynW2RCCJOD2rZt4b9ndK1hbW2N7c5NJMSVTGuEdvUTRyTIebu2zMxqztLLK5edfovaCd997jzs3roN35J0uT51VXDk1QuJJOyE9dPlQsfsjQXnHYH1YQJUIzJjAGgsOuPKBkiqEIBn2yc+confuNL2zZxicPku2uEya9xAi1F8SwySLkmp3h/LePcafX6O4+jnuwX1kVYfTYTdjqmltEVmOdiJqeViKYkx1+zanllf49vIJ/u2kYKsuA6AUTXiHCzpaAqQJ6eoLE8I5lwaSb73iSLF4EnJpwHmMlbz9WYeb98PE0UJx/E7GIVN49mrrNEWrunUEZfu9o8yb2anjvJMRXSbvsN5jrKVhvIQNKoS6euvp+prVxLCQ5/RlwnLeoSc0XZ8iK4EcA1ODqItwoqxUYGskmloD3Qw3yKgzxRjLbllyYGoqXwcnVSi8CCK6wsYNtRH5PtIejwA0c07eY3ug0WAQoa1Ec40jH56xV46v+PbG838fqlpzOD/35hHv6Uuv7Vu/sHV+vUcJRVVV8fthzZFSBV0NK0BkLCyusthJWHQjBpN7DO7v0R0doEYj1GSKKCsCvzSap0LglET2O7i8g10YUC4sMhksMxksUegFxpXClhbhI2uyOXH3h9M9N4ya5uy+ASvmnmzWDnHIztzEwwcVR2YGcdgc60xsHqMJfZm/76zfGl2T5sUj9PnGwPeH8IL2KjP4YjbGnY2UPh+o3RDYbqlQLC91eWJ9leViSv/ubbL791GbW7C3h6hLdLyPhDZkDA3hbFqDVHgpSLqKfGHA2soK9doGB0urjIYrjJMuI2Mw3gY9Od84vfPrR1Pjo70QnzdShnwEoppJOAN6/GMak9YzOX4/MY5l39RYtvf0cwd/TcDi/Fw9zFWI1xJHMLhH7hUu4nzQkQq2jWTY7bOR9zgBLGxuMrz2BfreXfTBHspW0V0LLniDH3nfnCg0CR4AGbTzAlNOIEqFkGPUeBfu3yHPe6yvLLF86iTjk6d4OOizrRJK56IDL2h5fof2i3lE5DF70FzIWDOy5wPm2nn7FTmLM2c/3tXPfjyWu+Xn14x5oOboBsDsPQG2NvF7rukNhIBcSXpSc36hx7mFBZZJ0Q/G8OEt/L0t7ME+0kxQlCQiaEnROP0q6mcIBSoFneN1D99dJF9e4vTJVU6fWGbSlTz0BfveUXgooy8QzGQ158Q3Dn3oj5kV7FvgZbYHHZ27889Nu0Q9bh/9jwEkv2nxPgCZzWGSmBsm7f78SAVnfx6GD+feavaKOB68DDuYNQ5hLbmHPppcLsB7N6g//pR0/w7a7SIpgABEzOwFgZUeKx0+04g0hCU6rfFa4WU8wnUgrEUaj6gNlCWiNkgTmOTSOXxVI2qBkCUiKUnLKfatbdxnn7F48SL9Jy8xWl3kQGkqEaIaQr7A2YHPrCuP7JeC2ftzLx9+5Xj7sCpL3n3rY/b2JuxNpty+c5/TJ08y6HeYHoxxS4ssr61TFob+cIHTa5bx3R1qqUnwaG3RuUbLoAlbjrbAC6ZKo5OMtaU+Kw8ekv7wHXo375G6Ek8d+r993nAAWOsOxeoq0/OnKC6eol5fxPS6+CwLERgwC1H1vpWwQEpErqGTY5YXqM84psVp1MEEvblDevs+/Zv36GxtkddFOMSaO3wRUpCWBnX1Juermv/s0gVWVzv8+L2PeHCwi3E13hqsqalNjalrrLEhu6oPDHStFEopnPBtCGb4T7QavM38/o/ZNb+xJymFiNoQIV5+UkKiJB3jqYoa56DT6ZDoDFNVgeqT5ThClgoc1NYgkRghcd6Smop1Aav9BVKt6dSepLaIJA0pRyNLRzgCxVs4ZDcnvXCe/MVn0WfPUCea/toaKu0glZqJwcXwlsao9DJsBkoG9XalJUZ70t6TJJcu0RntU16/Rvnzn1PfvIuuYVWlFKJLoRSpsQyGQ/aGA8ZFwbmlAZPtilFdR9FTidIq1BmNdT4oxpsaqSV4Q57mSJkERx9NIqKhFbNHhFNMEZ19H9LGHVMJGjTQhq0wW8endVgN0wTGZUjFPuyGNHhlZJ9kqQ5UYBdi74KTYNFS4qxF24pBrhn0enREhp5U0Bh0BIMmGKkxflpJ1MYCnTdehKee5kEByYk10v4AVAAN2rjy1gCbofON0K0VklprOHua7pmTDOs3Obh6nd0f/wRxb5NuZVjXOR2RoPD0eh0+8oL7t+9yQkiWdMJDa0JGAIJWg5A6GAPOobWaidJZT6J1zAQREXMfmDbOhZAqIIhJifCd4/YUvffkKuX+gztAzcUnrnDv3j2k9Owf7CGRqBim5bXi9v1NBB6d93j+1depnef9X73Fwd4uWSdHCkkvKXnj6RHKF3T6HRCa8n7C7t94/EOLcGHeIkKGodbZEjGbSaLprq6x/NpLrLzyEsniIgBSa5JOnyTvBSFb34iphmsJKeicPo177nlWft/AdMrk5k0e/ORnHLz1K+z2Fr42YA2ursBZku4gAGuAs4LKlzzcfMDacIE/OXWOf7t9j+3pBLzD1jWuYUXZwCArTY0UAp1K3ngB1oc1uzuS5V54IIPk+v0Ov7qeUdgASIbsucdsnM6BEYcMq6Ob9szGj8b3vIHWFHGELj4zZD0hitNEoW4JKAepc2TKs5hKzvY7nFpeo2cWUFsGeXWE2N1GTw6gmoKt8LZA+CoYtzIwzFAJiVbILINOH3oDlpYXObE2YLowYFc79oVj4l3QyHGNRkYYB3NuwGNOGOc2tJZ+448YNEfaU8yrNRw25I8bsHnk9vPOQ1uPJrU1c75O60HN1bKBLQ4/29FPCR+01IJAvW+O9sFLlMhYXF3g+SdPsT7ZJP3sQ/Lbd8j2DpDTGlU3TLambrGlRKNlIpB6LyjnZYp+rljq59RLPaqNJcYbZxgtXmLPdhmNCzAVzsm2P5o+nIE1POqgi6Dt4Jq34liYdzoOmavRMJ0ZcXFPP3aP/4iyQuM4tI5OtLRi3/lGi0YcrsrMyZrV99Dxdvtcnqo22MjC1Eqz2OkzHHY4qWFw9y7Dn18jfXAfubeLKOtgezX3a9rDB1BaAL72we4RwVmSAkQt4aCAO9vo7AvyYZ+FlUXKkxsUZ8+z01tkp5RUVWRIElkyR4RBWySmfaoZLDM/HeclemeEFjF3nf+0U/S/T/HeU9dlCDcQUeupsZAPAUmz+hwacfPssAbgEPFk/XHYROwI7y3GGpQQdKXimcGA7tUb9K5/TrJ5HzUdI50JYE10FKPaQshiqlUI40dET0PE5AshbayoDdJapK2QVPhaIpRElmPkeJf0wX30FzfIz5xh4cJ5tvt9pkrNgW3RzZ8tTfF5Z28fGrHNnJtbmw89up9zqL+CuIx5V72d93OMNPG4vji6xjZ/8JiHjmEuZRV8FhAoCb1EM+zlrCcJJ8g4UXXw79zF3bgFu5voeoRkBpaHq82NZOHnpAdUFDZWIBLkQYp8mCG+6OI7iwyW1+ieXKE4uch+T7Oja/ZNjXmksecfsJFeIOJtc+30mLb7sqs8rih5vAeLzhpGe3sxU1aY78FmkKhGk1IE9r6QITtXU8Ng/sSaHnrGR/fGZp8X1pAbw3DfoD7axvzyfbLNq6TuIVIUIOzc4kkAalKPX+ni1xZxKwu4pUXEcBHRH0K3j8i6iCQFIYMma1FixmP8wRi/t4/YO0Ds7CG2d2B7Dz2akDoXtFOqKYgStCY1Je79feSdGyxdvED/6SfZX1tjzzvqmdbx0cc98qTMrUGP+dBjbcLfrhS154ZZoLe+zktrPYadDp++/zmp1nT7HR482OLUmZNsnDlF9YXn7OmM6d4+D0b7nLt0htXFHrkWFHs73Phil/FoTFEYdN7hyWee5HQxpfvTj+nevoeyBQ4zN9/DT4dm2l+ieOYJJlcuUG6sUHUzvJKRlUZMzR7Y5C7aCpIQQdIkDhA+HExZJfGdHJPnVCsLVJfOUG5uk3/2Bf1PrjHY3SVzBiGaA4zQrrqy9L64x8na8I3Ll/CXNvjLn9zi3sF+1O4MzGMb0q+GkHOhkDJtx6h1Zoaz+ZAoSXqPE4pDZ8K/Zmv8DWdp0GhR8bTSOcmkCotdLgXTwiKlpJPlZIlmIiBVkApJWTqM9ThXY+qw2FlnEMaTCUh7PTSCgXNkDXBhg1q0ihRC7zwy0aSn1ul//Wt0X3getTDkwf0H/ODf/X85f/EiT1x5gbX1kyHFN8QsQ2HDEj5mAhIzoMK1J8AWoRXdtXWGp07ReeIJdt55h+Ktjyju36fnFBpBJ0lJlOaTe3e5t3mP06liSYBLNYWDTMtosIb4u0SBMBmVjRoOSpEogZaA1tTWIqTG+yByq2VoZ+OCXgq+Ea86puKjngfRdo/GWAPaOKCTSvbGgSrWSTXdTp/JriSRoKWiqEM2n9qYNh7f1BWiLEFBt9elWwtyGfrOOx8/F6VPGoevn9F783n6v/M7uIU1tvcK/vrP/g1C1Vx59WVeefkZ8qwbpoyLp2ORvtg6PoTuVUKQdvIQwyglQmkWl1ZIz6xhr91m+29/Qbq1iawNnTwhIaO+u8145y4uTzkhJDrLGQuCCK4ITqJyDucdJkmCo5sEIVqldMwqEOKYvVBY5xtzFyFAaRUpyseO2SCFZGt3h80H9/nam7/HqLDs7Gxz68ZVcI5EaGoTzm00Ei8FRe258vRldkZTbt29iUwzdNplfLDPwrDLN19MGHR2yBJNKhP2rieMfwpuz6JdoOtXMeuMt0HMWgkBaUr34nnWvvE6y5cvk/QH6CwHD6YqQopZrUPYmwwtFE4U57bgNvOIgl6PzlNPceb8Bao/+C47P/kJ2z/4IWYrZFnDWtxkjEuzkAVHKZzzKOcY7WyzsLDAt5c2+Bu5ydb4ILB/vENbg/OekbFY40h1wqWzjmcvlQhgfwQXztoQKrifcOPjIa4wWGuoYpji8epLgW8EI2evMO8dtCouc87SLCqjMQ7nncSGWg0zgzW8Z52lroMOkXQwUJ4nhwkn1ALLNmWwr1Fv1XD3Y+TeJrLeRdoxQhRAzfyOImRjQAU9LiFEFI7WSJ2j0h4q75EsLjM4eYLq5Ar7Cx0OOoqdqmLqalptq+gQteFSPG7P8nO+wZxb2DhUh9rvkAs9V+/HX/k4yiy8aeYQPs4pna1boV6HwLejIRuHjNVm7AUQWAuoiyKKBSt01mNxUfLkiSFnx/fo//W/Iv3iDsnYIO1cvcQMpo968dHvdHEvlPjSIzCIsQcFanuEvLNL+uk9esPPWTz7LktPP8P9pafYmnaRLoRhPGJQx3Ep/OEx7GIKVtE4h9F4b8JyHp0Pom2ORoxYNm8dU3HOUE72kFKHky8pgg6ZkIf0Blz09+ZdpNg7NK+2QOmch3wI3GlPzT11HVIAaylY6SjWFzN61z4n+fRj8ofbqOkUNR9aK8IhAQ1HykdWU2MjxUoGuejIqHMWQRVGZFkhxlPSrYekt27T+ewzuhfOM7jwBFv5IqVXwdk8DLsdBl68O/z4fnZ40rhdcXQHW+tQmFLMvtW0wZco3vx9izE1uw83YxIABVIhY0IAGQEcpVQIOWqAHSHwshm/jRabYDY8587723aAWQhHOC+X1tCdThlev4V8+y16d+6QFCOkj5qNUbvQCYnPcuxgiBksYheWYHGIHw4QnU4QIEbgjcWXBX40gr191N4Oem8Hvb+PnI5R1oTDzNoiyhIxHpFtP0Tfv0/n6ac4OHeW/TSliGFb84zA2So1t2/E3hBtX81KO+dommF+bHwV4JtrwftH9tz55Xw2qeaq8Zg1VBz60f6satPOv2GWsbi0wMLIsPhwgr55E3//Hmq6R+oKhKijIweNeK6PAFy4qIA5PTOBRTgBTiD8NKwjUkKRIiabsHcLdXeB7tIa+cnT9E8tsruYsZ9AJWNYWwN4H0as469x/2+eVcTXDrUPj3bkY9s7ZEo93uJRfgxVw3YmjhUV5oD3GO8CIylsAoQNJ8zLJmW3khKd6KAtJKIOVayqBAYKLi126coTLG46eP99xNVP6VR3UO4AgW2f3eIwymIXNfKJU4gnL8Hp87B2BjVcQWYDPAl4GX242TrQ9IeKPgzWQjnFjw9we7u4zS3Km9cpPvsMffM2eRUyADfsOWlqpCnxk33kg7ssP3OZ7PKzbCpFaR4vn/BYH/6rM2MeKTbrM33q61xYUzy9aDmxNMSMCt75yfv8/EfvsHpug2deL3n5pWe4lF9k89Y9nrn8JMnnnyJsQZYusdDvoaqKajJlf3sXn3Z44snzPLe8wODnH9G9fpOkmoR+Ep42xYtXGBImGxtM3nyJ6VPnqHtdrAgstOAThlnQCIc3M6+ZmyKGp4v5+d+AQQK80vhugj17ErO2THXuNNNffcDi9Rv0zXSm5xYPuRJj6N6+z5q3vPb0RbafvMjfvv0hByZkUdVazdatQIGNEQW2DSdvM+vaiNYpiVJhrIMIBob68rn4G2vayIiBVHEz3q8DiCOUZ28aqGd5npBkHYTbI1WauraMRwWFnVLXBVVZY6xBe89K3mWp12MpSehOC/IkRVqDFzZ0jFBYH8Kx1KBL9+UXGf7O18hPnUEmKdZYbl67zgfvvscvfvp3nH/qEv/sv/k/s7K8HhkAIk6aMO1cXFSj+QtekOgOQmc4H8XlvKO3vIr+xhvUL7yA+/GPePjjv8NMK2TeQVtPOh7RE9ARmjVv6WQ5OwIK2dzKUXuL8Z5EZzhZkyUKqwWJ1KhEByNUaoQKA1TgwYWUq9JJrDckXs8h0MdQRFjmnXDR1JidSpfGU1tHL/XsTyzWGtIko9frsulDRhiLZFoUTIuKsiwpyxJTlXS843S/z5nBAid1xmBckyjAhbhdHIhGy0RCevEUve9/k/TJK6BzcIJbX9zg7mcfs79zl5ufvM+p1X/O+SeebffdhqTdsgnELBuLiKyp9hQUQCnSxVXk66tkly5Q/eV/4MHPfxpVxCXnqciGPVZVxqAoGaaKgyxhRwgqGRYFYy21MVRKo7QnTzvYEtI8CxNZeJAaJwQ6rhoh04fD0WQmEBy3pA3ec+vmdV59+RXOXnqGt9/6Cfu7m5i6InGKcRWyQKWJZCEfsLe9z9rJDfb396n2R+ztjoIR64N+y5m1iqdOjkmlIE1Tyk3F3k9B7RgSQuYl0zgJIvCLvEzonD/L2h98i/zCWbLBAlpnKJ2gdBbSn6sElaRopUPYmJjp3SDE3GJrZxBDXJCRkmxtnVN/8qcsvf417v3Zn7H/dz/CFYa6rrC2RqYpPskQSqFCbADjvV1OD4f84WCFvygKblJSEzJ8mbKiKEqSJGW4IHjzBUNHOapSgRYk2lJVkrsfdjizDTmat1zFlo0nXMfoKBpj2N85QGoVgC0lg4ClDOPKyzC2pXh8RhQRJ0PLRPO061xjoM9v+sJ5XF2hvSfRgqUsJ81X6dzbQ17bRNy5jSoeIv0+gmJ20iA8zWoR3S5aoVVPPDiXCBMdRzlCTHdhnKD27sCdq+jeMp2zZ1m+eIrh2pCtjmS/LlvXcObAh7Vu5gQftlDmXOO4lrUqFa397g89NW3bNKDKcZOlwONiOG24eOMOhdCvmb6Cf2T8NMyipv5tNhbfAD8zTZQW6vEhjKkyBR5DR2ueWR1wojSkP/tL9Mefkk4NuhULknihESLBywQvUxApXiUxhKYJnwj6KsKGmGzp66BFVtUIVYOqkBND52FBcu0hvctfsPL0i+zkZ3EqoVUMedS/aHohvuZbp7h5vsZ1l80C336jcYSaduFQex1XMbVhezM6+0qilQxsWBUy2IXXkqA7IkQQHZUxBEJEQfq2zo2TNT92Y5/OOaJhToK2lv5kn+yLm/CLt8hu3yKpqjbUVBAo+k4qnMrwWQ+6Q1x/Adfp4rIEkWgQOoaRGkRVIqZT5HgfMd5HFrshLKe2yNpADbKqScYlanOP5PZtes9cYf/0JcZ6GLJ/zCZ302s0opCN1kPIpNU4fXHE+tkslf6wiGi7PgnieDu+PoQQIphmWUj44BzGGGZeV1gzwl4U6OrEvQw5A+dkZAM0rzUpn2mE7RuATATUUwrJxsICy1VF/613ST/8mHz/YcgQEu0RJzUu7eCGC9iNk9jTZ3BnTyFObiAWFvFZHrRQ5ge1ABHZiKI2+MmY6sEm1a07iDs30HfvILcekpZTlDEIaxHGkBQFam+PdG+P/OnL7AyGjL3HtLCUny0xzQj1TSDVbP1qRvChEk8MmuvMj/njKt57TG2RKgLfTUXa9m/WSWbvNTVqKzxDLlrw+NDzhNdNzLCWI+lVivxeSXL1OsmdW6jpDspXSBFdQ+FxkhASLAUuUbiOxnY0Pk/xqQ6ibTgwFsoaMamQkxpdebRxSOOhrpG2gHqCqPZhsol4eJfO3RMkZ88yPLfGwVLCgfSUPup2zu18TfmPMtU8TWPFveXLJ1sY38c7GXWSsLR+ItjRdY2tDdYEbTR8yIinPCAabbRZAJ53gRmNcxjnqVyQKmg04jwCqRR5lrDY6XJmYYlkW6B/dpX07kdou4XwFY0ehhGWOrO40wP08xdJrjyPW38CP9zA6gE2ikP7ubHftMs8cxkCS9QjQGjI++G/1ROIiw71wiuIh1u461fZ/fnPSa9+QbesSFxIvoBzSGuQVYmaHMDONtk33uRumjKtqjnbZa48DqRpYq6bWj52sv72xUmNz7psDBI66YS8m7N0cp0zl87yjc09/vrPf8i/+u/+Dbe/e4/vfOcNLlw5z71rcPfmVf7D3/yYb//x91hYHFIU8GBzTC0y1k+e4uSwR/b+NbJPbpIUE4jpvIXQbYIJI1P2z59j8oevMzl3EiPUnFD83J4SAZWgixp70IOXYZ+xxOxsc+05b3OE55RUnRzzxDnKtSUmP+qx8f4nDOoxSs6tyt6ijad/+wEn85xvPXOZ/b0JP79+i4kNGq/CeZRwqESh0iyw0YUJ2aKMQ9goJyGDlo1s6uckQWhEkve6X9onv6GmjQCvsGJ20lKbsFlniWBUhgwFWZqQZkGhRcuEh7sP2d+f4F2Nx+G8xQvo5V1O9HqcEYrVzT2GeU7e7+CnIyxhQns8XgqytRUWvv17DF95GTUcti6Ex5N1Uk6e2ODebYsbTXF1GPzCB6TUReen0bYIxmJ0uGkowSJQ9nAhO4cIHZ6vLnHuD/8BEwk7P/gBHos2hiv5AqPBEOU8o91dutbTH/S5h8MKAd6gbE3toKotSM2w00WYkizLUEmKdRaHRosA2AQFaY3yIWWg9RLV0HuPsVgZXWQBNUF4SXlBbaGsHf1csLMnqStD1unQHw6CyFaasLm9z8OdPayt8T4gEYn3rHW7nO/3OVfCcOch/ZUldLcbhIFdyBiGtwgl6X79Wfrf+wPk0kZcKIPTcGZjwO/97vNc+zRlWo7opLI9eW2zHAjaFOPNGGyQ0MMhB3EVi0Z3tr7Oid//PR7u3sHeuUs5mbAuE84sncE4z73yAVQlIs+ZphqpJdY6lLVoZanTjCyH3mBIORbkvR4uCmkLnURdZw82AA4Wh3WmFepUx5w9qqoqzp46zWtf/y7vvv8OX1z9lIODAxKhKasqhGbhmJaWW3cf8NyVKzz/jd/lrbff44vPPkGTUkx2yPOM1aWUN58tgSkqzyn2Nds/EvjdmA3DhwXORsBTOFB5j6VvvsnJf/CHyG6OqYpAc9UakehZdiIlg5ixak6tG6Owdd/CHG/CYpojwbhRBQaHJD9xkvP/9f+OnRdf4N7/8C+pb93AG4ONqThl1FpSQoGTTPYP2Fha5s3lE/ybu9ewMohJH1RlSMeXSr7+XM2JoUEpycMDWFgQWCe4d62LvSbRpmJFCt4k5QNVc907RsfYhxKLqLdxtWzOaiGytoRUCB2YK0IKtFTRmQinBlI24SzNyf9hIy66hszDHAmQO0fPQb4D+toB4uObyLufoyd3A1gjq4g7+NDficd3BD6T+CyBPMOn0dFvGFeFRZQOWRrE1KDKAlkLpFWIeoysNKLYhf376BvLLJ86Se/SWfY2FhmlklLK2Wn8I/z9I2UWqzL3nPN/zZvlc1bMfCD8MRfnDPVkf3aSGhkrEPsLaMKPGkaCaPW5xNyyJWjYNq1B0rpHs1cApEgwtSeppwz2R+Qff4x4+x2yvREqOpZIDSLFJX1sZxHfX8ENl2FhGd9bwvW60ElBx1Nd66Co8aMJ8uAAub+N3N9CHjxAFttoM0FYgyg9orQko89Yun2f4bNXGJ19lr3OKpXXIT1uqxExM6583Hcbbtk8G0zEhwtjL1KeW+aROHKdBrA7vpKkGUsbp9twX28DG8+UFu9rhPQoWQXQxjuITDOpBIhwuqYQrcMvVdAigajhJKOKSVz/hJDkWcr5jXVW9w7IPvwY9fGnpPu7KIL2H0LhVYJNO9jhEnbtBP7ESfz6BmJlCZYWEcMeqhPAJZDB+LMOV4QMmWZ7H7+5jXhwH3H/LvLBHfTBQ3Q1xlcGUZeIskYXBb3dPTqXtxg9/Rz7CxuMvWr3uDBofUCZ5ouYhTodXX0Or/TNOu9wsmEEzeR+j6tonbCyeoIm6YVzTRrumATD2UinD68bGxxJV1UtHAWiBVBEPNlvgEZFoNs3e5lSmp5OeKbToff5dbpXr5JORghvw9WkxuYdzOoG9tJF/JNP4M+ewy8t4pI03udxrmJsrzgfRJpCkiIXlhBPPIEfj6juPsB/cZ3q86ukd26THuyjaoN0DrVtke9VyP0D9DPPoDc22JeSumWez3bfaGEd7r1DjmL8w8ff/ZG19ZiLNYb9h7toLRFJYEAJHfc/IdFxvxAxXW+wD+cOEprnOLQdNMDNrI2VEOTOkU9qBjtjkvc+I/n8U9LRJsoVCBxCijZbn80V9SDFri0gTm8gN04gllag24Mkx8dw/jDADL6uEMUEN9qhfvgAbt+BOw9JdkvSwqBMjbBlCD2uJlDskuxtorfOkj/1BN0zK2ynlrGLodn/CW03A1SbElfNo901v802i/Gxb40CRBLANq2R0iISG9nnrt3ZaAAbH9m0DZnJx31P+pCwxMY5jAlJLLAMUs1gUpFeHSHe+Yx861OUfQg+zGcklKrGne+jX7tM8sxLuNUnqLMVrO7ghZ6BpQLCDJ9rIGZT4Sh40zRgq+clwfcSZN6DtRP0Lj9H9dFHbP3Nj+jevk2vrtA2MC5k1KTMyg+xByNOf+973M86jOsq2LSPacujlkEAK9pdsWnxYy3Oe2xVsjrsok2wT7SQoDzrJ1f4x//sH/KHf/od3v3VB/w//+//gvNPn+PsqVUejmom05of/fjn9IYDpnWJTQak557ED3ukX9yn/8UtusUUGvHwqCPqkZikw8HF80y+/3XGp9Ywfl4mQ8xMuebg3Ec9TT83nFsgOowlJ0WLSzat1UTdxEsF1uriEPEHv8Ptfpe1t95l8WCfTIbIAlwYH7r2dK/d5tzigD/89huce+VZRDdjeaVPqjSdPMWYis5ggPZQTUZYHHVlMKVjWlTUdRV86GjTCyEjSxa8kvzVX/3gsX3yGwcxNna1QuC9ZFqEtBNJqtmeCJz1aK3odjpIGUCS0XRK5UxUyg8ZYTKdsJh3WPGexd099LRCd7qkKpwAChPCc/CWdGOdxT/+I/rPPIvIcqwP8eQynoA98+LLXLhwkc0vPme0e4fBwpB2eRY+om/hOw2bJmT0mZ8AswUCBEIpdJYjlIYE1p+9zPj+Vao7O1S7+/RJWEwXqZUHoTBVjSoKyDS600F5hTQK7UKspazHdDoZwg2RSYZIEqyrwwkoGoQnFRLjbKBTOgs+CVohx380HMJaRBTf8gLlwDiYVJ5+prhdGKrakfckg/5COFFUktHeHsZUAQKLjIgsSVjIclYMLDzYRmJR66sooYPTaUPGJZlJ+t98hd73vg+dIS5q+OA9SFg+ucE3/+F/wRsHO+zf+4SllZXYO7IBVNvdJxhic9+HuBnMTmEFoHQaGB7Oozod1l+6QL2uKd57COMKkYTFPVeKonbc391h3Mnpra7gEoGxFm8cttMBWZJ3OoiqIO90guCnDwyOwCASQaA5xOWFDFvOzxaYYy2eP/rTf8TBZMrnn3zIvZs3QUhcaaibjYGw0WV5j4dFyb/47/8Fyjp0Z5FiuotWoETNm89aljtjtE4pS83uzyR+C0QUrwUIrJhw6putrXP6v/wvGLzyUgAf6wqvTUwZreLHg9MhdQgzkEITDCcRok5j2MS8mREAocYJCPPWxVSWXoBIExa/9jr5ufPc+X//9xS/+jneVPiqCLpROkXoBKSkdpbx7g5nV5Z5ff0kf/3wAWVRhrUnS3nqvOOZc1XIbOdhUkpOL3m2N3O2P0zpVJ46ZkfrSMkrUrGkFH99jD2otGZxbRnvgiHjnMNZh/UWXB0jwQS2DqLIrnUkmnAkiRCBGSCkxEsdKf/EEICoQSZACc8TizlLrNL9rISPPkXdvoY+uIFyuwhKhAwZZVzm8csJbnWA21iC1WXE0iKivwKdISQ5CB0dLwd1jZ9OsPt7uIc7iM1t5IOHyM1d5F5BYmuErRG2gOoAdbBF58FdkovnGDx1kb2NZQ7wUbhtNr5/3dgPPw47OTS4TOP9i/kX5wHd4y3Oevb3p21bhypFh0+KKK0W9qrwGhF8i0wNmvnSnPaLufqLSD6KBkt8hl63Q2/cpfvZp2Qffoy+cYtkaoKzLzVedzCdRVg5jT15AXv6DGys4pcGiIUc0UmRiZr5GNF4F97ja4+fWux+gd0+QNzfRNy6RX33JurhbWTxEFlOEMagyl2S/bdI7m+SXn6GvdWLjGUP48RcyNiszL/iZ08V/m7DZmII5ZzTJaJV1XxfHvrmb1+UkgyGw1CPmMZlhh+HNJ3ehpj0qq4wpsLZyOSIoRzWE7L2xMw/TZ2Dwd+w6EK4TidLOZlrTueK4Xvvk966QVKViCYsRKXYziJu4yTuwjn8xQv40yfxK0vIbgdUAIFmYqQNUBJYyeQasdyBM0tIew4xrbEPD6jv3af+4gbq2jXU3Zvo8Ta6rkII1eYeevoBg4e7pC88T3LqCfZ1Tm2brFczML0pzf7bgh3zc7D9qD/EqPHuKEhxvEVI2bZ7OCt5dC0JgI7FmBAeQwT/G6298H4TVhQAHmLYaWAAGKQQpB4G23t0332P7POrpNNxYMYgcDrFrKxjn34S+8yz2LNn8IsLeK3bGjW9NoctHAK6QpNHh1KIkN1SAsMFRH+IOncGd+Uy5dWrmA8/JLn5Bel4jCpLpNtBf17RmUxQzz2LOn+eba0wxh5aDv2RPn18me/Ahk331UA3SsKwE9LhWu9xTkDtsYRxXsVkCE4oHGEPVFIGxqpQge0iYhChlDEJl2ikglqm3movY+X0Kidu3sf/4j2y+9fR1QgZT/1REqsltqMwGwv4K+cRT19GrZ/FZAsYlYGITPg229Bce8UOFN6AKZD1GEb3qW5cpfrkM/S1+6Q7BUntEJXDG4MwBarYR+/toXevkD5/js1MMnIhM2M7v+Zs4aNb5iEB4y9t5cZmPopyHW9phFWFlCEsUsoW9JuFdYf/Qsjr4cEYPufjmGse0KGBrlRkDy3q/fvIjz4j3bmOsjtAFTJkSk81BPn6k+hXXsOfukLd3cCpHhbZZu5q1rSwJrnZH8zatgFWW4hazAWnibk+EAKvNWiNTVOS14bocxeZ/Ozn1G+9Q2/zPh1v8bXBuynSOjr+FuVf/SWr3/gGYrDIWIioPTi7/nwlZyFwc0DrIUbjMRbvWe0qlhOPqi2mnlJVCbayJJ0uSku6/Q4vf+05zlzY4Pq1W1z98CoP7m+TphkHu3u88/a7nF1Z5cmnz+KXFzmpEjq/ukqnLFHCzdk34dlMkjG6eI7xt19lvL6K800u3/i8AhB+DoCJPkRc07wPdnToDo/BtazLuqooy5qyMhSlIV0YcOHUyRAI52cAfZVqkjdf5mE3wf3sLZZ2t8msa9dmPCRFRf7RNS4sLZGeO83nt++wfe0mtS1x3pJkCb3BkETqSFTxuNrhbDgekMJiaxP28Gg4isbm+DVn/L8xaFPFVLohTtNRVWFi5kIzLRy1s0it6Xb7LYxbVyXWOJxoMgZZZJqRCEVnOqU3PkA4T11MsVWJcgE5dwg6586y+Md/SP/yZdA6OnyhM5oNT2tNb3GJbv95qr11tIykex/qJlqjounkQJmKZkbYYGNP+KhLAgIZY7slgiTvsHTpNOrSUxz85H3U/V1UUQaHH43GUU2njGrLYj9DKoVSEu08ibfsjjxKxrhMnSB0joz/U1pj4sxXVsX6OLSXWG9jrNvxlDD/4+B3YeALPMJ6aiWY1p5eLiiNpSgNCwIW+gOUDIh0URThFNJZvHNIIM1SUgS9g4K0LBAKqvEEIRJ0TJ2oMsXgW6/S++73obMQnBpmiyFNq6uEznAFpS4j0HFvip9qTlhFBEdUw8RhZuTHKwrvI0AQn1FKVJqQDRboDyR25SzTH30M4wIhBCmgvWAoJJNxQT60+G6HOklCKs9Bn9HOCJ0pTKZjjK0K4JFQIWOAlLjYJsTndkQB3GPsQ4DBYEjSW+ZnP/xX3Ll1FSkECYrC12gRLDvjLFIIxsWU0bVrnD9zhrS3yq1bt1DCMOj3eOnKgCtnPgMBUqXc/IWidytBOxv3hVkaeicVnYuXuPjf/Nfk58+FTUUKpLOBVaFUdFYVwocTdRF1BTw+sl8DmOQaYzRCQSI6R76Zh41/4InSYrQimdnaBuf+2/+W+/9ymb0f/AWuLEIKeBtFdpXCK09RTDD7iufXVrnrLb/c2SZNFKsLjjeulHRVcCbqCryEuhLc/bBLOg71ctHZcNaB9Vxyip8fp6MhFDLpt3/6mNEJEdMBWoe1DlNXUNsgYuZD2CjO42w4SfbIeHotME6013YiGLS5Tljp5VxeXCG9WSE/+ozk4XW02wY/BWHw0mFyC6czxJWTcOlJ/PoF/OAkJAt4leNkMFKbVLQzynk4QZeuRpoKigl+fwtz9xb+s88pP/mc7ME+qbV4U4MtEWZCMt1FbW+RPPcs+VMXeCgdZRVpss11mTl3nmbpEjNjq33nyO9f4ogcDbc6jiJ1Qm9pI/TZodTJwUH1Po6lKCxK04+41kNrjLNGY2Mm7BtSBwdiTmDsZZ0eT6/1SG6O6X7wCdnWQ5S1CC8RIsdly/gzT+IvPw1PnMOdXMUPOvgssD58m1DHRsNZzEQf8QgNPlew1IMzXahWYe8J7L09zPU78PHHqNsfkZT30UUFW1P09Cb9vQPU5R30hefYS1co6yYA93CZP/GerdsEoKR9VTAfmjNzbJtvHq/L76PDDrRsn9ZMFBpQkd2eoBONMTlCBMe/Xax8yBro3Cx1dgB/4nx1PoSsOkdeVwxu3iF5+z3S+7fRpgpgi0hwaQezcQb5/Au4py/hT2/ghwN8o3VCDDuaS2s0H0bSJJhre1QJRD+F/gri9CJcOY+/9zzVx9cwH36IvvUZabGDcgbhJqirN+hOpqjnRugnX2Bb96nqmUBkE67RAjJN+x1y4t3c73FsN85Gy6D6apz+mabQ3P1p9KNmRQqN1nLuAMHPcOCm36Jz5JsNyId1WiLIPeS37pL/6m2ym9dJyiICAxKTdnFPX8a++gr24gXs4iJW60NAJPhWdJj25WbNY26czzkrzNAWL8F1Ovgzp2F5EXP2FObd9zHvvEdne5OkrhGjA/Ttm8i6ZKWqUFcus6UicNNedO7+Ys5vbg9sZrVjfr5y5HGOqUid0F3ZCGAHFueCc2OMwZiYAdZ5hLAgLM5VWEewARxY67GWAPYgom2mWp0qrTS9NOHC4iqD7Rr7V/+B7v49VCMwLMEnCtvLMSeX8C88Bc+9hF85Qy07+OhE+tkCMWsm5kCThmXpNaQ5Ll3A904g1q+gnt/G3PiQ8p13SD+9Q/awQFcVrrTgDMpUyMmIfP+A9VeeQC6kHGCoj/gx8z/jzcO9fw0iN8+6CU5uY2z9Nr32ZSXOvaaNhJoBDxzFiY7uFbNoiPY9IVBY+kqT7Xj8hzeRH10l2f4CZQJg4wX4xGFO9lBvPId44VXc2hPYdAkvMrxvUm03C2bjB84OTPzhT8Ta+bZeDew8s0tpp5JvJpLSuF4PkaVkeY5d36B4913stat0xgckxiJ8gZSQ3biJ7GYsPvscDJcZC4lxM4bt4bD3x5UGujjeIq1hodih3q452Nnkzk0DHlY31lg/tc6g22VvZ59IFeHEqXVcXbB9/w5SeXrdDpu373H13Y8hyRmsLfD173yL7e0t6HY5tI87MCpnfOYMozdfYnzmBFbqdssI+4fDeod3hto43JyERV1VFNOCyXhKMS0pyoqynFJMSqbTkul0yng8YTQpmEwKRpOCE89c4P/0z/8ZqWmCu0PSESFkSB3+0rPsqAT/41+yuPUgZJWWMo4AT7KzT/72h6x841XuS8UXt+4xxZDkmn6/gzUWrVOElBgTM9V5QZIkqHhgJlUEi7xoIzOc+XKtt988e1R0rr0MG9fIOyo8nQwOaotxQS8h63ZJU0VRlNS1a50NjyfVikGWs+RhWBSk3iJlgvQOO94HIdEO0jOnWPrjP6J3+XIIufDRCBQzcVcRHRMhJV45dN5tDWIfJ6EQMr4mMJUhTdP2BL/dJEVjTMY4bQfeerQIYrpZ1iHr5nSGi/S//00O/uLH2Ac78VTHgjesCMXYGURVIfuDIBjZZGyJUypJFGmikWmGFQqHQ2qFRqG8wFrE2ASXAAEAAElEQVQ/m/gecPYRY+O3Ka0j3iykvon3C6yKonKs9SRTb5nGTCO9YQedJkwmE6qyCIyWeFqWacVCnnICSW90AN7gVUJpatxkRIYg0dD73ZfpfPcPoBtYUI1YamBARdhGgBcyTNCkw0z4tQklgOYEtCyrkPVorg/bDdQ3dnSImw0aLB6ERKY9MCULz54n760y+nd/BwcjRG1RtWM1Tcmkoy5KzKDHKNHUicPZDm4zaHwoLUmzBO+CIJaUKghBShlpmwTnTTTjihlj5ZhK3u3xs5/+DZ999B7T8RRFED3WEhbznKI0TIynwDIcDrly5VkmteTa9S8QfkKn0+HkqUWeO/cA4UrSLGP/tiL5SJBWdSsD1WwETki6TzzFpf/jP6dz5gLW1IFBEMMFhDNBKd3HTDIxO4P1DuFC6F/QfYjaTXPt4YP7ShMKRwQDVLspijY9NICXAjkYcPKf/lOE1mz/u/8JqjKIfZUFpCkQFkAzHtMfDvnPv/FtFvqGrYPrnD1VkSnP7e2UTu7Z3hUM+5J7n6ZwP5yQe60ghhoQRYidNahj7kffjmAIT98YUB6hQEqHFik+sSRYfNQd8fgIEISNzAXOP02suPMmANDeMxAJg12BfndC8uGnJHsfotgDYUA4XGKwpyTq9Qvw3CuYtcv4/CRW9cGnbT940WQJigaUnAF6SI+XGSgP2SJ+eAJ56jLiytfg7g3Gv/wZo1+8Q++gRluDdBOErVFVgRiNGI7GyBefYUtrClPPhkfrMBw2jBvnIfiKX9In807bI2FHx1eUlCwMOlFkrhnP0RhsTsAiiGuMxVR1cDBwrS5GsEfC+tIwTlykegRmQqCUp1pwsuOxb/2M7vvvke+PUd4jRIrXPaqFc8ivvQ4vX8FsLOK7KaTROmhX/UbkPhqfsvHRYhtJmskYTiy1RHRyWO3AxTX85YvU7z6JeftndB5+COV+yGxjarqTEl1Mkc+8xmayiqldO2eacKj2BLj5x9O+Ni/oHKDTyKrxR/r+2M3TGUQIs3uFKjYGVLAYggj9XD3m6uYa598frqP3IjAwhKBTlnQ++Ijk7XfIHm4irQEh8SrD9JcQLz4Pr7yEO38O2+vidAwtbQ6f5LzTIQ45W/NSH8EBOeLkJRKx0IV+B3lqBa5cpPzVB9S/+iX59g2Scoq0JfLWQ9LiXRamJfKFV3mgexjbsCLn3Zejxc3uRehF2dK4iGCYmK31X0l5HJTQ2A+HXwrZpZqazo+85gGCHTkbGiFlayYlndv3SX7+Szq3bpDUVUxyIakGC4g3vo792mvUG+u4JAnP3W5h89eP9WidvSO1bqs939J+BtwI8Chcv4e4cB6xuEi1to792x/Rv3uLpDYIP0E9eEBmLEvOwXPPsqnAWMcMHJ41WzstG0bNXF/5uc+FfZnHDYLfrniB9wmeBOIaKZRBixqdBvZNyCQbZq30Hh0siPA6URuiOfxwloYZLl2NtoKFkaJzdQt++Rbdg7uI6OwjBbabY08s4597Gl55Gbt2FiM7CFRjWLYZQttRMzfVfKO90zTY3D4Uwm+6mDxHPLWCPvss1fX3qd5+m+zqXdKdAm1KKC3SWfTVGlFNWX/pMnq1y66w1K29G0PrDo3px3XG/AeCBEQL1PkjE+K4S2vjz0BT3/gej/twW98G5BHM10sBHaXJJxL//i3Ux1fR2zcCw0bUeOGxCfjzC8hvvAgvvIFbvohVfbzXYR6JR+8+szeadolrXfvB0G7NujrDwqI9NDdvmqds+scrhVpfQ2YZdjCgGgzxH39IZ3sz2ELTIthY167RyRPspaex/SVGXoTDcQGtPERjE0ML5LS1OGbbFCCRjgsDSIWhwrN9f4udvQNUniC0oBoMsSYkyxnt7+NxaC3IUo0tKx7u7JLlOVVRoDsO86Di1he3EFVBlab4JESZCCewXjNZXWf89RcZnz9FrRPacYDAlAX3795jc3uHyXjKZDRlPCkZT8ZMJhOmRUlZ1VRFRV0ZqqqmrCvqug4RE81hSlxvEYLy5hfUwhPy9oVkQOV4Comm01XUqcZfeYJdaxE/fYulrS20t3EMe5T35HcfUH34Medffo6PP4XJpCJXko4NWqY1IWrIWBdCca1lyiQQCVQSGNgqMNGsDTqo/teIoP7GQsROeBILAfGCwnpK6+jksLvtqauQ0rrf6yN0ynS0h5QR7An2BoO8w0qnz0ptyAoTjEUpsd4znUxI0pR0fY3V73+X3lOXYrx2QzMW4JtFeT6SOkwwp8MjyRYUaBbWcNpvY6xzJEvinKARbQ6LrA9pup2NjpFtn10nXbRK6Jw9TfonQ+78q/8RubOHwKPx5MBpUqbjmqSr2dMKrz0ejdYaXI3SgiRRyESjUdTSIZWOma0kKg14r4up/2STxeIYi0BEAbAA3lh8cMAkTCpHJw3CupNpOOkYDjukac5oewclw8IhVWAgLfe7nB4ssDKqUaaGKF5qpxMAVJqw8PIVer//PciHNKFObX/SLOKxkUV0T4RsYxVpALW4gHvvUVrTOGGtCTmXfcU7HxYT0bABosRTkoFPIMvoPPMUolIc/Pm/R5uSVCUBrPOeyXhEf3kBl2dMhMTnXZSXJAKkUmRpgiCN6vcCL0K2CuEc0ouQEl3MGDbymBfUqq659ukHTMf7rfHknSWRgv6wy/ThPjpLefLMOc5ffIIbN+/zYGsXV+3R7+VIM+XS4j1yt4lOFFWZs/W2IJ/UOEIoTqMl5JHkFy5x8f/wz8nOnAcxE2tsWlxKhZAJQkWKslD4QF4O7S5D/4jGARKzPvPNn3GjdE1/e0Fd1mGsadWecIax4hB5zol//I9wxZSDv/5LpK3wzmFrArChU1xtONjZ4exgkZdPXuGD5B79tOTefcGnNzV5BzqpYPSwQ3o9RfswZpoT01gj2lQ7X0E5fNmmRRutgyC4F/iSjfM+mzfN7w2zi/hNQQiJ6gDdPYX85QPSD98lmXyO8AcRZHG4fo18ZZHsW9+gPPN1Kn0a7zs0Iu0+tkPUn4zjoXHdaA2gwJYKfdcsyVYmiIUOcrBGfuEZ6tc+YvvP/ozBp7foWBdEbr1FuRrxVsHA1MiXn+eBTihtcyoRn7M1ng47PE0LzBzAI+bYocwmj3PmjqMEQCSAzs2Z3dE6zPY/rdUhC69hKTjXOPti5qy1ucI9WkmWvMH+z/8zgw8/IilKhFdAglMLmAvPo//B7+OePkWdJ/Ek2EPMnhGyAjWbYmO5hGu34TWNkSwA4SJlOBqw2sNA4Z9cwG28iD29wcFfLdC7/RNSu4cYV0i7R1Z9whIO+8Lvsi2HWFu367sQgXnUeKqNQX+oX9oT/qOmdTPqZ/vCV16ODish2nBl1+iDNLaDICoixD2oET+Mn1dC0XU1+sNPUL98i2R3E2ksSiqszKk3ziC/9Sb+leewiwt4nbbVaK7zSIpfMXMaG6ZSMxtmc8JHp3IOJpECejlcOolcGmLXV5n8zd/RvfMh2oxhWiC3LNm774dDiFfe4IHMolYas+i9Q5XhcDcSx32sS0tIEuG/484A1lZFRDb2/KrQ9h9xHIpDdmBbeTFni0DQF2sslZhGPZWS3t4+8ic/o3frC5K6BCRWKqqFFcR3v417/RXqwQAvdbRZmY135myW+Xu3FlFTZ9/22Wzliqtt/HrIHBY/oTV+eQn56suI/oDRX/4FvRvXSEyFmjrkQ0v+wbsMs4zy0gU2TR3v06RFp3VqD4MNh0NG8PMAkziyxh5DaZqrHc0SVIKQISOs8uFAA9HYjnOVdcztSaGyIRNV+D0DuntT8o+vo995n3y0CdRh3U4UbmmAffo8/pUX8U9cwWQLWC/By1kmGSHadbGxb8Lrh/t0NvYeHYfeS7zIEZ0N1OVF/MZ5yg/ewr77Edmth6RjA+UY6RzyhiHFsPzc07AxYFdB5WZMtscyZ5qGhEN7SqND+ehe+GVAym9ZHtm/ae8zBwPOvTq//M/tSfHFREg6RuM/3kK89ynp5nV0DIny0uFTgT89QPzOC4jXfhczuIATneD/iQYsbpx2fwR8CfVpAdKm2ocWj7m6Nr7J3B4w9+0jzSBgOEQ99SR0OlRJCu++Q2f7Pro2SFGid/fh2jW6aUZ1RjJNe0zqKvSfEEEjrdHCEyKSW0K/ibnXjnNN7WnHCb1HPRrRSeHUxoCV1Zx+bqknu+xUExIpGW/vsrX1kCTPmIxGJIlkY2OVfDqm3x0w3t3joK5YWBhw64sbbHhD7Q0+hod7L5lmfaavP8/oybPUSRrbeWZfTvbH/OgHP+fDz7+gMCWuClIQxhqMDWGujVkj/JG+FK5lpjY6YRLB6OE2k7KkI3U4XJISnWd4Kdpv205C9cwT7NUV6qdvsXiwh/Q+mtseXTvyT79gcX2Vl19+gfc++wzRScjznCTRpGkS9hopApuorqkrg3OmjWBpwkC9a2yvL/f5f+OU342yhfMgpMc5cLWnn0JdGurKIBEsDQYoleCsQ0uJjSeFCYp+1mE979Ad76Cso7FLbV2hVU7a67Dw3W/See5ZSDI8MTUc4dStWSyBeN3oYHpPMJtkoIo7H0KyhIyGoifRSZvtwFkbrxLFI6Mx6OoqBkf44Bw2C7PUYVAoxfD8Oex/9ifc/5f/H9R4n1QnWG/IhMBMpywUBW64wFgKtDZoleGtodPpo3WCypJwcu0qpExBaCRg8Wgv8EqHthZutmYcU5lBYOF+VgSzXnkYFZ5cKnQEbQSwMOyTJR2scQEs8Q7pIfGCFZ2zLlL0aC8woFwIAxDGIrwjO7fO8I/+KFCzke3i2zp6iNboaCjIHmJITWRneQuNcRUNU6UUuPBda0IIWcPcadqraTZrQxiCcxavZukGSST9r71AuT1G/s0P0aKK/Q11WdPbPoC8h0s1Ig3CotJVJEqQyAjUCB2YPEq3zycJCLkUs4XUHeNCCjA+OODe7S9wJohIO2fReJaXhlgHMkl45ZVXWVhc4Z33P2Iy9pTjbdbWVzl37hzZ9DOeOnkjpC+UOVsfeLjvUAhsBGvwAiME2YlTXPjn/3s6Z88RwqV8SN3twXuHd6CkRmkd+qs5qY/0zgAoRcO90auYMyh8a2RFxk2zuQqPSnV0kmbm7mwZB5nnnPwn/wTz4D7VB+8gdchUgHHBABcgJhN2Pnif9YvnGZlzDE+8j/aGk6sTNkcpwsJHHww56wOrLoS1xRN+4SLrRiJ+DWXx71tE3PRnhkKjFzA/jhuDGhq3MFgITTiHiKmf5x02yKSgO5aIt+6g338HNf0UzxQpwaYee0Khv/kU4o1vM+0/j2UR78I6JMQsa4Vv22L2d8t6an/1M3tiZnWH8EClIB8gL7/K4smLHPzVv6P+0U/o70/Q1sB0gvSO5P236GlYevZZtrSMWhqz8uumkJ8bHzMntvnlf4kydx/f/nPodSGCUG1r/Lcfnq9jY1jO2B0IgZKCoanwf/239N//CFVUCCRCZrjeGvb511F//C3qk0vhpDfauS2oJVycXVENprVswjwXbTiLmNVfhPXV+8aAFnjlEUogFzXitdPY1T9h/Ber8OG/J6nvQ2FQW2Oytz9nJe/inn6dbaNx1sx80ng9IWQLwMz0bOba0ze1jUL27V4fvne8OHhz3UdePtKfoXiaZ5j13exgYSbO27riQtAR0P/iLtXf/pTuzoMQ/iEUVuUUp8+j/+T7uOcvY/MUhIzaaEfHb+MaNO9Fh3rOuT9U17iON+zFdqzFuYwSsNJFf/1Z6v6Ag3+rGd54B2nGuKlBSYF+90MWOxnmxde4P3YtDNN0wJFemz1zFABuhZOIjnjcwxsg+HjLHDg0B3Mc8RoP/dI8RdiuxREnuH0XiDyJqkL9/G26166TlE1IjcQsrCC++x3cN16n7PZaB2oGpQWApW01PwOuwl46c67jh1s7bdbfzV/zdpScPa+Q2E4GzzyFU5K9//F/YuHWdSgrpHPo7YT8ww/oZSn3s5Tt8Qii9pmQMurSabRWqJgtTUa9GOlne3erVfKV9GHb+k0nxKeLVr/wjVcWl7B2lWttySbqrCUMCkgQ9EclyUe3Sd57n2y0Cd6GtSdNsKdWcS8/h33xRdz6aZzKg7fTeOhz46L9Nd5AeP947GpuD5+Dlto9HiFCdtWli8hXFrArGxRvv4X/6AbZTokvLQKDvOnIBCzJK5j1HjveR01O2TqjIo4Zf2gDbKGddlzHHpyrYByLx+1owPw56peUNnCqreeXRRZIIBMKcXuK+cXH9DY/R5sthCiDXkgm8Cd7yDefg69/m6p7HkdG4zs0zdLMv8P7R7TBmjUtrvmCRx/AC6JQfNy32jeI++iszZs+bgdit4O8cA7SnNIJ+OXP6R48RJgaNQW1uU2SXaerU6Ybpxk7qE1I4FM38XhChP1BzoE1BFAn2OXHZ6cqLG7vAZvTOvpNIUx/erCN9WCcxxnH9GDMtCjo9rtkecbyap+llV7QiLWe7W7C1v4+ZVFwb3ufJaDywcbEC0o00xcvM3nxSeosozkwlWJu7/JQTSbs7+xSuBrXiL03tktLb5utuY11Pdup5gFdqKYlD3Z2WFpZhZh0RWdJGA0+MJO9F/hORvHcU+xOpiS/eIdBNUUI29rBnamh+uX7nP+T7zA+fZYbd+8wNlNUqrF7ewgJ3TxFy/BcSkqk8yjhEVpiEQip0CoN0hvyy+fib5g9CoQLbBslg7COrQWlEXRSzcQWFDbkWl9Y6OOMDcLQQqGFwlPTE4pV4+jt7JNMi4iKCbRQOOeRCoYvvcTw1ddweUTbfGhq4RsHogmlCScRYdGxrVFMu/H6mJ4NwLUpNJvOkd4CAu8CXdzhA3KpFMpLnK1jqEEAD5wC603QBFCKleeuUN//Bpt//m9JnWmFdY11ZDt7LOZdzLCPkA4tE4QJoTSIoMPjpUL6AAShE4QXKOMRMji8WmiciPF1x1isD2wQIUJjymgwOB+EiBMlyJRkPK1x3jHsd0IIkI36I1KTeM+SkCxXluT+VmTZ+KCtEkOFdCdl5Xu/h14/MzvNgdbZbAzfZiWdj3MNjmg0hFuAoFlQGxctbjZqXvHdh34AtFJYF7SUwqQPG4KzFokPoq1pwvCbrzJ+sAnvvY30JanWdAyko4Js94DJyZUgoGYF0hq0EoFRohKUUkE7Rmlk61B7hA3PJaSYo/wfXymmE4pJgXSBSuesocKxtbsf0GeVcTCpuH77PfbHhno6ZX19iUuXnmDY8Tx5doI0U1SeMX3gGX1o6BgRwx/jYookGS5y7n/7z+heuBiaV4ZFtOkD16D7BLQ4UAzDHPON4weBMSAAXAxXa3pLxNCn2H+NL0n8eDufG4vDx01z9pVkYYGz/5v/imv/t/uI7U20CbHt3gZaNbVk/NFH+I01euoSewf36fc36Xctt7YEH99NuTUp2U5ynpaaQWVI232vcXJ9g3Z+BaWh1/pDr7WtIGZgySH3fm5daOPno2ORCxiUGn61iXr3XdLiOkKWgMfmHne5h/7Oy/gr36RKz+HI8UhkFGJ3zNrXE5WH5voo1prGMp7N7cYZO1xbLwTOa2x/md73/3PKjdMc/PBv6d64TVZVMJ0ieYh+7z0W0gT71BNsSxXjupkTiTvcbnN7M54G8DrcfO2DfIXl0as/6my3aaBj+zTG9ayqsQfDwJ+Bd0DHVIhfvUX+/ofosg7Xkhl24QS88gbi979BeSICNs0tJbP9MfaVb9daG0M5ZXz9qPPRfK7xyo54LFJA6hBPDFHDbzP68w6dX/012fQm+Bq1fUDy1vss5R0mS0+wXZiwRzdNIwPDNUKvMcSUZvFHNuyugACH7BwtU+Er8hSboT137fn01TBvf/vWiW5ZG4+cyEa7RYS9qPtwl+kPfshg5wHSBwDN65z63EWyP/kj3LOXwwmjEzNxxqOTHlq0I6zBzUIl27V2vhZibu9pRkITJCciAC8QiEyTvXSeOv0++/9G0r/5FonZx4+nJELhfvE2qytLjNYucDAu21HrhWjXnpbFFQ2Lluni5/RtmnVcNuvHVzMvG8Pc+2bcQxNO0KyTwcdtwsTFXKhPAAUO1y58LsWhPr2O/uBDdDEGHCiFWVjCfeNruFdfou52I5ih5r4v5u7MIfu0yVga6hSFWts+Dp8LB5dRHNlBojVKHQoOj/tkiBtyaYp66knsd7/H6M//HcP7txDGYScj9OZ9Fq5f5/yLz5P3N5iaOmQyjXpM1hiqogTrcD4kU2j2YQEoqUh0EmxtFcTT3a+h8/92Za4FWxpBaMcW9GvaqjEePLO5GPtTCehUhvSzL1Bvv0O6t4loGDZZijl7Avf1V3DPv0Q9XAWRtM7ioxiCP/Jb3IdiPRpQs3UOD21cjxvzAoTGd5YRl17G9RYoOj/DvfURna0JqvTADvLmddI0YzF/glEOk9qE5ATxtKRx3FtAvrF7RbMnRF7Q/AbRPoVoAcrjLfPXFIdeF4+83tj/sX/nbD5EyO6bTgXVTz+je+cTtHmAoAQcPvW49S7qG1cQb3yHafcijpQZV+NwnQ4/6eF6NfSbhqIws1WDb+F8CIN1LsgeBKk6R4Knl4ZY43YPjfPateMSRJ4hz54mUb/LZFSh3/05qjrAVzViAvLeA7pZBt0c1k+ybxTW1RhTxxBqgfNBS835ED7toq9kPTh7vHPRO0NVTZhUFUmWhtBub9r9o6wKdvb3OBgXDL1hUQ3pK4lWwYYp6xqdKlaWFyjHU2QdtJlqF+puhWKyvob5nReYdjIgZHZr+y0ejqRJSjfLZhBc3AOb3prp3sT35/o+zLrZQagk+DqJSLh1+x5PrawhY6pxGn8Yj5eNOAD4bpfyhSvsbu2SfX6N1Bc0lrLw0Nvdp3rvI9afepoP96Y8GB0wLqeMiwkra0tsLC+Q64QkZsLDeaQMWnGVCWtRmqfoNCGRXw7N/GagjRckPhgCkhBWY7zjoPL00yCaW1YWJ2DY62NN2EC0bNJeCpZJWClr0rpE2pC6TUdH3+uE7ulzLL35e+heCKWZCU82TooIIsJCzCjdeLyQLUAjIjvDH6KLiSgaC0SxTiEFzkcx2/APJrIIlHNtBKOM/2qRg/Xgw8IotWT1za9zcPMG++++i3SCTtQLSCpDurfPdNBDpFkIJ8EhE5CJQKsEoXVw+IPMPUIoRCJwWKQJg8bNgx3HVBravCG0jRYS5wTOwUHpEMKTJ4KDaRAF7XRzvJdIJVBSIwV0HawgWLSWrPZR+yOmwdQamUgG37hC+uzzNJk0mmXaE3Qgmt/beYZoDbywB0qMD8ydwLgJWaCst6FtovaQczbEC1pHbRx1bamrmhPLXYa9TjDIRBg/SiYgc7yxOOtD5rNBj8Xf/x2279zGP7yHUpLMKzIh0Nt7DJb7uEGXVCVoJ9BJyAQiEx1Eb2uPVuFkKoxB326eIi4sx8208d6hkVhnMa22icfamtWTG+wcTHn7rV/SH66QdfoMF3MuXbhIt9vhZO9jVH2HJFUIL9l/H9QYtACvVQBbAak1G3/6Jwxfegkrw2mpaEVMRNTpCVkdhPdI52Kq71CXRqjWOY8XNizEUiCcaNk17QnEHCjRgDLts9KYzBzySDwu6iU4Fp58gpP/6/+S+/+v/w5pHb62YA2ucKCCs1h+8CH+wlmm985z1VUMsgmffK65uadweO5OJuykCU/0OjxZQe5C3WtnQjY3NaetdFz92PzSOAl+ntkwX2aZiR6NXT5MONZCklcS3t9D/OIdkoNPQYxAWhgAL6wgf/93sGe/jlXr2BjR+1jzSszmqIt/N+DNjElwpK6H6NnNzA4GrRUKmffIXn2DerjM5Ic/xH/wMdl0EoCbrQck777DUi+jOnuRLWvCvQ75w/Osq8Nu6qNt89WCNY/eKVpmfv7Z4xj2MwcfZiCU91G1aw74an4RAjJnSa5dQ73zLsn+QVxAE+zCGv7VVxHfeg23sUBIHTnvJhL7zzO7cONAu/hv43zPHN3ZaaMDb0GEzGTikJEf2186WE9J/uA1aiUQv/wh2eg6oqhRD0bk737I4is9DvqnmVY+zCPrgtBeyCeNJxyEiLl00kIE7RDVBBxFw0004I0Qc1T/4ymhWyKI3Djvh1vzkb8bR3J+urYZWWJfSATDqqL8wd/Sv3UT5argYCc55sx59B/9Pv6Zp7FJGto4Dph5DOHw7dsNM4BZPh42NT3dLqTM9byP+ididi0/c9sAnJKoZ85h/fcZ/WvH8NbbiHqCmUzQylP9+KesfbvH1kRSVmUAeKUAIUN6ZhHCuYVQ7X/I5iAt2m+xoaRv7LevYH62LEA/125H2oxZGwnicxydr81fkfGi8ejNbfw775CNt5EizA2b9zDPPYd//VXMwjBefxbG0N5/Xjja+2jreBwuHAxah6sdtTGUlQlZTooiiGkWU6aTIKRZlDWvvfYSp86eadfVdiGJxQkBeYp47grVwQGjH/wVw50HiLqG0QHq5k36q6uUTzyBSDuBse5ndgSewF6wNoqp23A4ZKJAvrM4a7Gmpsm09VWWBixth0tcPJsQhhYgjNSf4Ac0/oEg8Z7s9ib+F78k270XARvwqaI+s4r/vddxz78SMu41wFm0UZpx2jpvRJ/DN45jqFKTNaypBjS75BzLZka/mu1azR4rJKR9xKmnIe9RSo3/6dt09yqopqjxFuqGpttJWb1ymqIeUzqD8QLr4rpIYAU7IfAhB0SYi14hUREMjqEaka3R1PQxPMPfuoi5f44yW0QTnvQl950Pi1JAagXFr27R/exDUnMHyRSEw2qPW+sgX3sK8fq3KHoXcD5rr9OCVBH8ae448x7jfi0JPoVvABsZe11ihaQsDQeFY3dSsjcq2N2bMtqbMNkb48cjTncq/uQPn0cPF2hiOJq5JFwcAU1dtESeOUHnD7/HzoNdklvvI1yBrAzqYAq375J3claWV6h7S1TWoX3D4glzVHgX9+g4Fn3Qb1LqeG1U5zyj0Zid0Zj1jbWgveIcSkqUEJGcYSjKkrTKsd5jsSETbKyu1gkSR64UGAP3twMIjaBIU9zXXmCyush4f4JKM7oL6RxoF5otyRKyPA2zqbVrZhtl3FmiIROtGRkYqz6OJ8lsTZBK09Wa7fvbYR1uDmi8xzsT8m007CYfgLdycYB8+Vl2t3dZ2X6AcjZwELxDe0H++U2WTp3k4rkz1DdvM5lM8NZTFpad3UnUHrUgYdDrIr1HKUlVTNgfTcgW+vS6HRJ1TKCNA4yXpBDZDIFtU9aOpb5EWklZhPj1Tt4JGZCUCg8kFRkwcJDZmtTUaA/CR4AkMcieZul3v07v1InZUifiAtQ4gtHwcHGTCmt0QOzqymIrE9IzCov3YG1waq1xIeuRt5TOhdAoYzHWUtRVSL1VGeq6YrDQ48rlK3gsgiguKyRKZ0AdUvRGgWHRyVj6/W9y9+ZNxO4eXQkeTSoEyf6IhfEYsbRIlmVINyGNpxM60XHBjGeNOsWjghPqLTIJajNGzBlZx1SE90HMlbhktX6GYFqHds1SOJiGjVlrhRc6pFT0AWQbGMeydQyMI6ltyNAkVFDyt4Z0vc/Cm7+L0N1IIYxGSww6bF25eELZ6Bw4D9fubPPFzduMx1P2JzWTyYRJUVBNC6qiwDnDtCwpjMFWFltVVFWJKQ2mNnhXo5zjv/rH3+EPvv/9QBsUkf6bZNQITG3I4thBQOfECt3nLzP+9/dQDpIkQSNJjWF5e496cRhSuQNaaxIZwSmlEN6glETqECrlfNB/EN6jZBAIns+lcSx9SAjXsca2WWvwMFgYsrpxEqd3qYylN1hGMeHs+bMknQ5deY+O+BjnPVpnTB9qipsOTThptMZEoFAyfOkl1r73XbxW2MqgVMNviqaSj/Hi8ZSyEeEMJzxh0Lr4OSV8kzwqaJ/IxqD1bexYG/s/ZxA15m9wOgLw2WQTkz6ETHgEaaI49XtvsvfOO4x+8rcAUQ/ExjS8UN+9TbGywIl8ialP2a1qtiZhbVFREK00lg9GY/b6A15dO8FG6ZhuPqCspkecgP+FioCZGHdom7m32n+DdRlYNpmVyOtT/E/fI9v9EMkuXhr8ENTrp/Hf+iblyddBLEM8Dfbz12rd/ENISXt7L3xrUAV7LBiAbc2akJpoXBz6sghGsNMp+qmncVlOoTv4t35FXk6gmCIfPCB5+21W+gO280VGk0kQvJ5z2IWUMYV29LdiSFDDyDzSSO39Hz11O87iOTzNZwbilxUBqKMAXWvpBiBUP9xCvPse+v69GFKj8Z1F/HMvwpuvY0+s4FQ4LJi/o595DrSWTQwTbrTHIJyaeVPj6xJbFdiypCqmlMWEui5ZWDlJf+MiyOzI84SRIyWIEznuWy9TOgO/rMgntxDjGnX7IYsLn2BfWWJ7cZWQbbg5HYw/o5C29z6uY9FJjGGKjZxUE0PunY/MgGN2Mhp/Ssz860Nd4+c+9JgyA+PCZxpNmwwofvY2+Ucfkfg67H86xZ48hfruN/HPP0OdJXOUfA45OsFxpB3LHhdSVBPartHdk4Q2tbbCmJqqqqiLgmoypSwKsjzjxPlLJJ1+e58ZqBNXdg3Z8+eoy+8w/rMp/bsfQTVFjAuSu1t03nuXC69+g/v1ArU1WGew1mBsFUUUXfQpmhCV4JwpIVoQRwgRWCKRkfvVlqMaE/LI+KVFxmbLRovuNG8ihKDjHOqjT8lu3kRZgwesSjHnLsDrr2FW16KKW+Okx3DEeacRz3g8YfvhDuPRKAhoTqeMp2Om0ynFZMJkMmU6KZiMC0aTEdNpSABRFxXOeXSiObW+yunTp5kTWmnXtmYceiGh20W/+jLTnR2Sv/sbOsUYWVTI3R2yL27RX9tgvLCAaT3bZp2NIVNCHmoXEa/f3gMAF8LVj7nE2XR4DRftG5FUI6JPEN+eA+zC4VnQb8wPJtif/YrBg9tIX4HwOJ1g1ldxb74Br3yNOhmE/pqfh+0zNjdvVr0j2kcQHfXg8HsXznetF1gHxnoqE+zAfiYZJCEyMXy55Y2HtlY5rJ5H/V5CYUr4+Ud0DwyimCB3N1GfJSwvDnEXFtkRFZUzOBuSveAt2ODXWMI6aS14JzEujBDXOKFS4qM2ofM+si6/inLIUml2p8d/8jF7oQQ6UpBtlrj3r5IVN5E+ADZeevxKhnj+IvL138EuPEVQF527y/w0n3PyA7CVU5ExqTx7k4qtvSnbuwU7O2P2dvbZ3z1g5+EB+3v7jHe2cZMxoh6DK/C+RvoK4Su6TKlOppivJ+iFV4BkdvPGbGyYmT4GxCuFuniK7j/6h9z/f2xyYnKXxBi8qFF7I/yNG6hBzvIb3+HOuMKjDj1ME5beaMK1ruKvYWn85kUwnRbcvrdFiWBp1dDLUowxARABppOC6bQGD0pJ0kyjknDI66zDYUPSEiFxOFACqxW1AIPEnjnN9OlzmCQJMhe6ISo0R3IRuEsUaa4D8GgC3N0cdoXdRhIyDouQOU4qtErQecje1el06HUyup2MTjen283JOwmnz51G2Ll2lBLv6uiuquivhvnppEKe2mB0+RKdX+zTi+GLEUWiMyno3bzDuacvMTUlnZ5gPJ2gk5T+oE+W5XhnmZYlznmK0YSyqignFaNRSSk01dSQpcmX9shv1ruCAKJgUHhqERqnnDo6qwmlglFZI50nyTOyTOMqHXRanGdoPMsO+saRWxNCMYSg8obEC4YXLjJ89rmQJjgAhyF7E41fFxpGRL7a/Pyuq5q/+usfcuvGFxxMx4zLKbaoqMuSoq6xJmiMVFVJ4WukiacbJhpAzuG9wDrPM09f4NL/5f9KnqWhqyRILSERmNJinEGL4IgC9NY3WHj2WXb+7u/AenKdoPBk1tN/uE2xPCDJE/zIhzAwguOPlAgVjfM0AS+RXiK8wrsQQ5gKGQSojrGEtSSIrSohcF4EAEp4yjqIM+eZYG9a45wjyxL6gw56M8SHaOvpWEPfC1IbtFSakwdjHVIIeq+9jFg+FRw050DGvGONn+4Ip4PNzje3AP31f/gVf/0X/w5sSV0WWFvjbZPu2CCVxBrXTBVUc1FBcFxEoNftj+4RgLewkQoZkHOLDDo3LjpxQoCSDJ99gvG772C3tpGpBgUKyeDhPqOzlqzbwbuSNE1RWqBTHfrGqRDznSZYHxkrUcw6iPWqI87rb190orHOB/Q6VxxMLYsLi2ycPcOkqNjdfki/t0A92WZpbYlO1iETBae6n2DrMf1egkNy8IFFFA2AF8Agh0D2B5z8h3+K6vbwIjKLZExH33oR0Prl0Ukvq5K//dFP2N/fj0LQAcyTMW9xENkOIlyW0F9aqHAKK0PYGc7j4yL/0muvcOnM2TDpIzAxo00GD0v4YAB1ezkrf/Kn7H/wAW7rQeh3AXiHtzW2mGIP9rEnTjDYXeRvr9ZUKsWKEms93V4PvGcymXJrtMdDb3np6cs8lWvy2/fQaYLa3z7WfpxZm2EM/1pW3Zzjdqg0zSEgF5rkbg0/+5Tswfso/xCkwfU88uWT+G99i+rk17ByEekVbbpnf/hazWgAgZdE/XsAFXRqRIjFN15QWiitpzKeovbUtUVUjvXMcnJRIdU8A8a3IRlSJeizF5HfTZhai3/rLbqmRE4nqNu3SX/5c8793je5nhiMKQL+FqYt1oc6BMNBBsHQZpzJQOEXcX1tWXZCfGWwTZNpZR4fecyn2n5qHPL578+92Zorui4RH3+Gvn4dberwrKqDvXgF/a1vYM6s4ZScORFtvxHAj2bFcxXelrh6Sj09YDoeMRkdMJ0cMB2NGI8mjEdjRqMx00nBaDxlOi3wzvHmt77JK98+icqyueeag/W8xymBP9VDvPki1cE+6v0R1Nuog5L06h0WVq5SPz3kQOV4HwQDUcHybBys1gFqnMI5VKE5T/cQgArv58Jij6s0wBYxbIZoczcd2tRhBgT4Fk15tNMFIbuiunYH95Nf0ilHICxeauziCrz2Gu75ZzFZTsOAaV3nuef2eKw1TKcj6mKMqcLhxXQ8ZTIZMZ2GPismUybTgul0wnQyZVqWlGVFXVSYqubs6Q3+4T/9J6yc6c/AoAZgaBlaDqcgfeEC5e43Gf/5iP7edWRVI0ZT0ms3GJ5Yx1x8jn3bnIYG5kXTb86HVKwipji3zuJt8z5Y54LumBeR7v/VlMMzvek1/8irR93J2a/N0aEn1YL01n38e+/Sqadh65EKs7yKe/Vl3LmzOKVaZ6MBNx4dGp6PP/6EH/zl33Cwv0NVTamqiqquMHVIae2wYS1x4J1rISBB0KzAwvb2Nk2IZcs28eHOPgKP4ZBTIoYLJN/4Gvt376M/fo/MGSgK1IM7dO+coNfrUSmFa5hPh4bx4XHZtmyDLIqGBf9VrKqNYzqryuFJMpsn8dOPfF8CeVni33qf9OqnKFeEF7XGra7g3ngd+drvUOpeuGA8EAr/P/xMjiCVV0ebx9oQ4lFZT20clXEY66hqR1kaxmXNuDCMpyXjccnBqEB4wRvPneKlJ1fopnLe9J0BYR6E0LilU6hvfo/J1CDf+YzO2CCqMWrvAf6DTxgMX2WyMaTyNWjHTEsLEu/QEUoKIC+B7eFCdkl8WEettxhbhHCfr0Cz7/HlP90WDgw3SVYp/Ns3SO9+inJ7QJDhMAMBV06iXn0Fu3GFmnz+y4+9nRdBi+Xu2PAv//0n/OSvfoXb38ZPD3B2gnI1UlgEFYgaJQ1SW8BjqxDmGxoUbBwnpXKM6i67D+9z6pzHtrIdcX5KEYF2QETbRQT2Rvrseab/4I85+J//fwxG90mMQZUVenuP/PNrZE9cYqt/mrJ+HLNUHPl5vEUAo4MDRpMCn2im0ymp8Fjn0InEVY7J/pRxUWLjni6VRCoVIUCHsB5ko5EoAIkVihpJmWbwxHnK4QArJN2lhZaIATPg33mQOiHN8gAkz7GZPSCUZnF5lbWVNVaGQ7qDHt1Bn06vSzdP6XQS8k5GJ8/o5CmdPEMnIWrEeY9s9QACY67c3UNoyJZSWlTYh3vZPKW8eI7JjVvkd0p0ZCQL79AG8nsPWDh3itWlATKzWBukYqyHshwjhCBPJFJCr7MQ5EqkxBmDqWvSPCNLs6Nd0ZbfUNPGkwiHcEG41omw2e8bSGRQ9JiWYTD3ul3yTo+yMHjhkGVFx3m0tWgRF3kZmTAeZGfAwjfeRAyGmEj1El5iY7pTj0MK1Roxsj3Nl3gHZW356U9/xvWrn1JgKW2NciFNr7MC7T1SBlqXjU6KJVACNRIlwom9EprJdIKxBkQWWVYSL0NYVu0ihE44vbdIvNYsP3eZ/fffw2zvhdTHSiGEp7s7plvW5FnGdORIs8DISHQCUlE7Cd6gpMI5gVAxP4730RmdO6k5phLi0GlPKgMwFQznwnpqJ+hkmv1RibGOJMlYWFggz3K8r9CTkgXr6KJQziFt0BNyIixOem2Rziuv4uWM4uZcuEezQc3WmmgEx83fOE81muDKfYSoEBiUcFiC8JTDR4XtMEYcQddIitkW6z04oZgUkwD0CI0QwdlzzkWadgQGrUPqAL50lxboPX+FyV/9HUxL6GQI79GjKb2tLfr9Dq7aI9FLSC3RaRpOU40NQCNRpBcfNZegiWlRxw28Oc9waRldHNDJQmq5c09c5P7DbR5uPWQwWGAyGdPv53S6XfCGtd41TH2fPFegNNU+VF+E6zW5iYhzc/HrX6Nz6VJcrILQshRRhtw3Ok+0J70igmUHe/v823/9rzhowA0XJb0bQ5LGjp2dbDbij7JlaIRxIZVicX2JC2fO0mQBC7jNDDCKOA7OeRIt6F68yPA7v8/Ov/wfQviFEK0TQW3Q9x5ghkOy8QqT6Q5jX4UB4z17oxGdbk5/2KeclkynE378zlt8sbjEy2fWOfP8KeS/vnO8/djACO2DP1rmacIzZ4JDnxcIMiHolgr/7i3SG++j3H0QNT5ziMvL8DtvUK2/ghNLCB9CwpqO8dH4ED6s7XtOsm0TJpVnUnumU8tkUlFOaqZFzbSoqKY11bSkLCqqosCUJaYqMVVNzxf87pM56997ET1cwcV1u1GzauATlyjEubPo7/w+xcEU/ckHyLpGjack12/QW3iLc19/g/tFGp0+h3MurOuRjeGtwblIb60CVb/2YEXI+oFQIDRCKqRO4NdQT3+rvmwOFto+mXsvvtJqpLSCpEe61vvWUEmloH9wQPXu+6SjcWBhqoxq6SSdP/wm5tzJIPIcXUvp5yYEjZ3huXPtKrff/zH723fYH03YO5gyntYcFDVFWVHVlqp2VLXBOI+xrSIVmZZcePCQF+sSncVh6g8/a+P0iwTk2UXsGy9TbN1D3vkloqxQO2PST66yuHGK6fAMlRdHhnvYj+cdv/ZX2bRlYGZ4HN6HF49fONM3vdDU4sjfj3E25lG6uLk1+jZSCtJJgf3Jz1nYfYj0NoCdnR5cuYx8+QXq3oBGZyh6+rRggaCBs9ja3ePvfvgfuPbRu0zHe1RFSVU7amsj08W2p+q1NdjaNsx8EEEPgkRyMNpjJVhQM90qoqZWI+QoPDbX6FcvU20+pPzRAbLcCmmId0ckV7+gs3aSSXeVKtoQUqjYVw7v1ZH2IaZb9WG/n3vtN/Df/n6lAS/mKtPoxTQVeVy4afuvCKFnAykof/ILFvd3EDExwv+ft/9u0izJzjvB33G/4lUhMjJSi8rM0rKrBVpAEQShyCGGejg7O7T9AvuN1mzHdsdoa0bjctSCwxkSGAANgAQaLVFVXbJLpc4MHa+697qf/cPd771vZHZzmoiil2VFxCvu9evi+BHPeY4bjNGXX4JXX6YpyiBrYFVfi+de2paqyoOHD/nww/eplsdBt41vioa0YY3cO/3lmHZaQpw/2tsP+hbds0nUhdKa7Bw3ipw/j/3VX2L//h22DwIHoRwcUnz+OWuXL3K8cYZll0fSG5foSKBzYoh0DqkvIqWmiwL9jP29si2TTF3lBzNA6ZXB3YfoD37I0M0ADybDTdbxr76K+covsMzGJNeYxDFry9SHi6HA0azi9uMpe7OG+bJmtqiYzRccT5fMZkvm0xmz2YzFdMp8OmU5m9IsZvh6gTZLUGV9Y4tba79Cc30NikF8lChQY0aBI0n0HLN1HfPNX2Z+NCf74HNMVWEWx2SP7lC8t8n65qvMC0OVOkpKE0vdjyeuiWe8iTqHBH3L4MljKuoX4UBtZRtPqjeJY2Q1eLE67QZD6QW5cwzv/4SyfgzUYKDJPf7aeewrr8HVV2iY0GZhpOfvFsTqhVVYLJc8+uwzmrvfJfePCBZhslG0FekBvNQFDFqyYQ3neOCthEXdsLe3y1VpEF+2yzg860nEX5LtSm2E9V/8Mkef36Z4+y+xsx1M45Fljdk5YP7dv+DSb/19Pq273d5vX5TDFKBpHEeHS8QEJOhsWmFdkFFZnlH4OCYSOOp8XeOrGrIYRIPA1WoClYOaYEtURjm2lsX5beTKRVxRopg24C7SkRAH2wCMNYzykOUgYkgBH4B8VPCVX/oS3/zqG2wMBuSZxeZlQGQT5JSmlNwUXCA4klr9m7SDYmC/qSlTIaI200upDdjtdWbXLjN6/JjJvIFESqxKeTRl9GiX8bULHNgiuAtMKPSTRzsbCUF9F9Gc3gee3enxFG/k9Jw2ilCJIjagM4x6ChXmDWRWyBGm01B5qRiWFMUAyaeI92RuyQRP7j154ymDxwP1gUh27bnrjJ69FXKgU6ULNYGcWIKjIbGdiJoItQpNRKh8w2xRs1TIDBgPjQriQ4TeR01TCUpnTYDHOQAN/ChZrDLVVA2+ceH+Ega58YrGsn8QjP/MBynojVCeWWf80nMs/+TP8bXHSoFgyWrH8P4Oa5tjFt5jMhMN/gyHQRqDdx5DuI4SSNPURyhZjGacZgs6QUBMdAUAgwCdqzJrPONSeLyjNI1jWAiT9TWyvABVctuwnueUSxdqdQmd8DWG4UvPoGe2V5jVUxQ6GeThfqsEjhqNGq2DQyagZMJaSJsvGPuuTYto0zDa50hPA8tZ1SmIUYO1xmKzvFXUOuELNs8YP3+T5Y8+QB49gLoOM2CE/N49RtsbeBHyCAu0Nmc2r3i8s8fm5hq6DEYP6loHhI/PZn4GG/h/SvPqyfKMMt/g0c4jrl67gc0KHj9+zHhtnZ2dx6ytTXjm+jNYk7E1njLkUxQY5gHlNf1ccfM4ai2rHxSbW1z8zd8Cm4e5CoMQ0w4BnwjuQj2HhqRkCDuPHzGbHeE1AMU9nYBMDjsfT9SUmqeJc6jnQEhzWJZlu8A6NSoQ6qGKjwqH8xrQFMZy7pd+mcP/44/wu49iBZO4+FyDPTzG7e1wOZ9wYTji4GAfY4XBsMQ7ZT5b0Nia8XhMPiiYzxbc3d1j5/iIm9mUelmd6jyuthNG4orCASmy0Ed0JOeNKJSS4T8+xr7/PtnyNiI1ahV/aYD92mu4K1+msWfQk/fpK6cKlWZ854M9/vDbHzF/vINbVrhmia8XNPUC55aoWyKuwjcV2lShOpBvMDSgDWulY7+8QjO/RLlxhrbaSKpypbFcLYpkBnPjOvKL32K+v0/24DYsK+zxlOzdDxhfv8zo8i2Ol02QBXhMdI623AKE/G7RkAbrfXTqeA+uRrXGOY9fBqj6aTdtJyShBzvOtb62plHRa80w7cvE8ELADgkTPPV3f8Do0SOs94hY6tEGxa//Cs3z16ltokhMyrrpzri4XuoG/uz7n/Dt//3PqeZ7OKeBA0EDnxsQkavBIQbRKROJcAXY2z+maRpyfDAu40N1oxiMRQEoDPL8Rdzrb7Lcu4PMPkcWS7L7jxh8/AHjL51jKSM6At3u7AljlpTQzmCkf5/0jVPXU9Me6K0N6W/A7mMrt05dPdEfURgYg3vvI4affxIi/CjYHL10EfPma7jz2yFN9ClbXqNITqNwcLTg7Xdv8+HbP8G5WUjRjieiRIMkkPfHIEjrsIkiG2VZVcxmMwSNqAqflAFScYCWi0kEzgyxX3+d2eefkn90RO5m6GxBdu8hgzt3KJ9bp5YsoqC7c3RlcCSuIpOShiRyJj+Nf+qUWut7iGe8dGOs3S/0TxToGf19ThxgYC36+V3shx+R+zqcdTbDXbiE+dIb1Osb7bO2KYntotD2egDqleVySV0v4xz2HQNp59N+N2q8UQ00mIhKPdg/aIWHSU6a3qGQ1kSQjCC5xTx3i/mXv8T8T/6Ecb1Alkt48IDxo6Az1JJoB+Kz4LvHSI90cqyeOEtOp/2nXlV6Py1CMZvhfvQWk6Pd4GwzBl+UuBvX4MtvUm+cbflHVk/EPiYzBALu33vAH/zxD/j07h6z+ZzlbEaznEETzkN8jagjVJUJyPBkuQfxZvGVcnx8QNU03T3bTR96LtLjwZECc+km/s0vsdg/JLu/i3ELzOIAe/tTRlfOs3bzPPtIeA7t+So0WotJdxJtObC6B438PciKTD611t5LnrKO0pp9ujAXIFOwc49+/BC78xmGWbiAAb9ZkL38LNx4nro4A5q1e3p1/qDlmIrNo1S1Mp/NUD9HdQEkR3dwCkhvNYV+Rr23/Rn2WQwh0DTKwc4elpDiS6vjdJ994knjfLlxzuBrb7J4/BB7e4FpjjG1x0yX5HceYm5/yPj8s0yrPrPSf562feU8ZjzgcDpjMlljMixomgrvPbPG4Xwg2rdlcNLUyxqX54jxsWpssONMlIViDJURDjfXOXzmKoPtTTSCFVo1l7YECOkws8ZSDgbkJsdQt0FkAKeOrLRsbK6xllnmx8fYIpAaSyh1HWVWCO61mLRe9CiIuvDHeOsMjZuRgstKIJ3WKCOXRUl+9RKLjz5huJgRcwgQFFvVlI93ydYnTI9nNDkMigwrhPhha7wGHtagGweOVec13Oe0qkcBOENIoFDw3iJeqZeKWDBlxqwKwzEsciajEYcH+zTek1nDwBjKqgGFmoB+EWvR3LL2xqswHsehTJSHXWnhtBklpVJEQRiITQMMt6mWMaqe8uEFbMAQOJe4W8LR6LUtltmmzSQzs2pqGldHI4NIqqFkNsdnGZ7wDJpQFQRS2vXnbrL3Vz/GHxwFSBjhkLCP97CTAdYGB5VaweQG5y0Hu/uUGXg/D6WaVVmkTU9YJO402cCTkEyafWSYDwWrhMoJy9qzVhjqytBUHrsmrK2NyLIS13iWbkY2GSPzfXCB8JlYKtmUGcM3X4N8QDqAuntHuO6K6IpCNu0dFbzzrYTTaJQJwdOKD1GuECsMpbutQG4NWW7I84Lc5hR5xtpgGEj0TGdUmUiK7bVBY66lSFhnxmZka2OKl59l/vAhufOhIpL3mKMZo40BxwI2s6HsbfSwF4NQwt1IQe4SCqXBGME7jTQ+pytkUxRsYXI2L10jW19nWtecu3iJy5eu8OP6LcajEcVoQlU5potPkLrixpk8CK9GWHwSyKMDb1TwltfGsPmNrzG8fDnsQg0lJRUfDDmfyM+i4iaCzQJvFQp3796jaeqkcpCErlVIpXrTMgxLcdUITMtFCGTOZZ636zPSIZE4bQJRWLhe44Vag9pRXrrI+M03OfyDf0uQGWGNGe9hucDXDbox4NkLF/jw+ChEbWrPeDSiLAum0zl7R0eMhgPW1yfM5zXzas57H9xjumxOdR67nP2fYYW2ezV9bjVqYzQgHbNj4O3bZPs/QThGxePXDPLadfxzX8EVF+lyrbtbJmmaoKs1hgf39vj8B3+G3/sQ9RWGoIyGu8cjT9Le9L0+KYjHe2FWrVFVU8Z4tMVyacc70B6WghYF5sUXqD+9y/LwEDPfh8Wc4sji3nqLzXOXmEmBiw5EeusoGdctKaxVrPqgPEHvYI6r7gupdNJp3q26mIgWtXMyhJ+GxHdyAt/fyktroNjdp37rxxS+CjIrL+GFFzBfe4NFltOaNpJidhoVn2jxIzTesXe8ZPe4aRGivlVPonHgOyOPZPRHxVXFcng0p64rUlw4pTVq7H5nZgbYsl0rkTdeoP7oI4qPdvHNEfaown78ORs3HzPduE7lOg0+OWqebsP3DN6e9vtFE5+u3o8Vo19gJdiQ1pYkJZPgpM8Oj9F3f0J+fIiID2f5eAQv3IKbz+DyqFR2l+/uLb17AfO65nixpHYunI8oSIrJB1NfNZ0LfXM/lk41lnrpWRzPWidnq2PENdSprPE5DGTXtmne/BKLB3fJjz9DqwpzcEh553NGly8zH231SPb7T9GNRRBfyaEY7tMZa6dtfJw01PpttX+r0e+kE8XP9aZ2hDD98+9xrlqEYRPBDcf4555Frl7BtbI5LZIgR5WQct4ZxaGyS7VYoJFgvd8fiVxyxgjWWIwJ6Z4h0GTJ85yyKCmHI86fPRtSp7JOb0qIjYCqiNciSmYFLQcMv/Y1Zu9/yODOZ0jTIAdH2M8+Z3LlEsfDMZX2V530nufJ2SUaYdp75XSaoD/jei1C90Rv+s0glF6RT+9QfPoJuY/BFpPht87CK6/SXLpCY3oVvk4ijdprK+ocx7sPuPfed3l0737UgeK5lxyfQm/s0lrv+ieAcxWz2ZS6DgTOmgJg6ZyIZyTRelUEKSaYZ1+iunuH6nCKOVpCNcMePqb4yeesnzvDfJIzU39Chkrv//1xk9YWbm/N6vx+EU3TkkpyM+rk/elbtQwEo4I9WtJ8cpthvQc0CNCIx18/h735PH7jCl7K1gGSvq0rv/X1ziB/auepl8tQRbgl+U2Sy/dOyBP6ey+N0AJWldw7TFWzODwEbeiFKXtioScP053SR4xgr13APfc89eEe2c6SvKmQZU12OKN5/8dsXr7BrPpCsG0/vRkYTDIuDjbYWAzJMhsqOy480+MZ8/mCqlqiAuWgxOSwqGe4w8jBEzMiDAav8V+1wHvH3tYGB89dw0wmdDa/9H6P9q8mvnshHw4wuYFltFvjePrGUVce7w3aGD5//1M2r11le3ubFLSVdHXpgr7tkmzvSauz+SZVuExDETMpoj7enD3D/MJ51vZ2KbQhGEiC8UqxP2VjWePnSxYNlIM8cDDakOJqTLCXnQvBMlXFLxtKYwPAJP/pqd8/P07cm0ic6HE4ajQgbQwMs4z9ZWCXz61lPFmnyB+g1ZLaN7iiAHU0cQNYsWTeU25sMHr+hVidpYMsGUB9IBMLIxurECTBRkiVEQSNTpJBOCqpCORakgW1pTBBOfUm5FoPMsMwyymsIcsybFlS2pxhXnDl3Bb9A199cBCkFB7xijWBeT1F93ObMdzaZPrsTWbf+R4qlto3GG8wDiaLBQ/EUBiLGgnlqMXgtaHIBtgyJ1NQF1NOIlRPE1LgtFocR0GxqjgRfBRQBkG9oa6FcWFoGsN82bAlsLm5TjkcMZ/VVI3j2M05C60i5iOsPz+3zuDGc4hkQV3sbZZu7npPJLSRsNa/4wPkMKTDBS6iGxfPcPH8eQZ5wXg0IhuPuX37DpfPr/Hcsy8zGI3IxgMGwxGDYkhZlgwnE7Ji2BJYBoPOEMp1Rc4Z1ahchVxMW2aMn7/O/Dtv4Y/2cW4ZfPTeMDxacjxoMAastRhrGBQ5vhxSZgMQC5botAnpRE5CxP+0IYyuabh9+zaXr17l0rWbLKsKP59xYXubo70dnrl2lbXJBocHh9yvJjx6vM5vvHQWkx1i8CwPoXkcxiUcVzEDdbTGhV/+FcQW4W8JAkWCLY5XxUlAtZhI8NuabQL3794FH9LYDBH+GsVr8nSHb9D+TnKq9NA+ENKj8iKUbfQnI0TJSI3j2qiybIIhIjZj65d+iYM/+xNkMWsdOyqg3tHc36Eq19j2wsBkzF3N0tU0R0cMhwPObEwCN8RsQb1sWF8bUQwmzI5n1M0XVdo0PlVC03TDEJduf6ziG8F6RBAKDNyZwicfYt1jIEBR9dIa5pVXces38FLwhDKuna4qBCh93Thmsxl+sQt+D7RuVZ6+wuGTodMqqcH4TlO1mFc0i0VI22kVxACtTXpuegYwyMY69vXXWH7yKfmnMzK/QKczirsPaD79iPGtVziqaO8DJ5ZEO2AQ1alVJTGtl1NGvZ28/8mrJzRfMMyiMRudKkEZPElTroys4eB7P2Tt+BBQvLG4zTPkX/0Sy8mYhFZMxloaD4nrIaViOO9ZNBVOm7gPNH43RWBt/D1ULwzlRixiS8hLTD5gzgWqysZ+xp2rxBSCbj580mWtYK9s4l57leXd98mmM6gc9uE+xeefMN68RCXZKny8HYAu7z+MUxg7MaZDZqQqN19ge9JAXf39afJcewZIjqC375HdvY11NSBoluMvnMe+8iJufYNUqldXLhzmTXxQdxIPTL1sqJYhuqlxn3YmhaWVrzEARQxEWFtgs4B6zkcjGp+3g91t3bgmJKnB0o6/lobyjedYvvccw7cfUfgaZgvs/QeMdh5zNNykFtubm9UxSU729Gy9nfjkh0+ptUtDe240TWfVyXnrBr5zAyb9MqY1P96h+OwzMm2CjDQZzdZZ5MXnqQaDwOWQZPZTHD+tuFWoa0e1qAJ5tKShNxRZyaWrlyiKkuFgyHAwZDQaMRqPmIzHjMcjBqMRg+GQwXDA5to6xsTKau1NYnpV/7mhlX9qDPbcefTll1k8uM+knkM9R+/cZrj/IsVwjZrOeA3XTi7ZVeNa4/ievsOm156C0lgBiUgP4ZdkbJKDQL5YoB9+RDk7bNGNvhzgr9+AZ5/HF4N4foZv9J+iXSvx/HDqqZsl3k3Bz1acA/R+7xDD4W/tLoE1IL6mmk6pF3UslEDqPfy00TQW2TgHL73C/PN75LN7mKZG5kfkDx8weLDDaHyJhfaR8925rnHgTroM+v3XNoT9n6m1U/vT72mArPLI42Pk4X2sTonkPCyHnsGLt+D8dXw2wZMIY1e0p3iPk3/G7InGUy+WyFPOX5Purz4G5hVrobAwKjMGgwGDQcFgWESelJIz6yMu3bqO80riWzx5THW6y6quq0ZhfYS9dQN3+w7N0R7ZsoKmQeYLzO07DI4ekxcXWH4BSOGf1vb3j/lX/8P/0UPuaUjFbQIqyRhBVGm8pyiLrnKVhvMky2x4TQSxgRg4n1eMD49ZczXz8Yj1PIuOiHCmubrCGSHPi3ZLh+EU8kFBkccy3OJbx6N3nrqpQ4q8zbh882ZE7wCSqvfGetCa1OrWOG1f70jNk34V5jJoaUnTDXZQPSxZXDrP4tPPyKs5IqEipgDZfMZGXfPslSu89enH7Db7rJ9ZZzDII8ghVGK2RgMhtvc0DvIix+Ylg+y0iIgB68GqhPKQKBbPIqb3jAplVkenTZazsTZhWAyZHx3hnEMXFbnzWFe3qRNGDJOrV8nOniNVBYlJ/UGpEZ9SxYK3LWhwrbGSlIuqaZi7GmdCBSaLkpcZ3/rG19jaOkNZZpTFGDUZH7z/HleuX+LmtWcZFSVFnpGXGVmZk9mSsigYjUcxSqLBsBcJkCUTFBTvGqxzaBZWlM0KssGIySsvUP3wbWRZUfslmVewBcXREUMixJzgOMhMxnCYk5VF4FpQReOi9xoIdGtXnyoUPG33iBgjRdg05VR7mM1hfSxUIkyXFUaEjckaZTFgOT/EooxsRubmZKpkxrQlLCfP38BOtpCoRBqRWBFLI2loz3CQdiqDQJCgWDnfkMVokc1yxCu/81u/yq/+1u9iTI7awG109yefsDZS1s4/0yn50PrI0ViKUiL0XxKXQuAb8s5hfUCQiIIxhrwoMOfPMrxyFf/WDtQVKoonp9g/otyoMb5BXEMmhlE5oF7MKPOC2aJhdnxEUeTBDRInzvQNjlNsRoTBoGQ+PaapakyWsfv4MWe3trBFQVYUVHXG5/eOcE3JT45v8rL+mEwWLB967BxSmlwSmJvP3mJ09WqYpwidhaR8BySYhMxFQDGJS0QV5xyP7j9okVHJIF9b3+BXfuVvMiwHUWCBupgCGSPmAbUW34si1WQZl85fwECnUMe+aly6GgaCRpWjusHFA2307LMU165Tf/geYcaj0oZBp1Oqo2O214bkoswJIto7x3Q6p64bJuMBZVkync7YPzikHAxZn6yxax6d6hy2hlP7bE9p0b6JO7X3zQ6dlC8N7oO7DI5uI8xAPH5k4PnL6NWXacw6qYRlGz2KUdXW5RJT05YxTYxmSfAiezC+l7rSgfiT9yW5/axRMoQSoKqol4twjbZKVVgrqScaZQGxqoC5dpnmuZep798lm1b4GA3OPvuU4ZVrTO0Y99SI4gkz+6lj2UtZOuWW7m56/w+v64lPJc8GrUKxYogQyOGzwynzd96jiKggtQVy8wb6zGU0RWE0jV1nCncbj6iYNq2BIG0PDCYbkZVbmHyCLQZk5YhiOGGwtsF4bY3J2hrrZ9bZXBtx+cyAwdZZIEPEt5Ubk2MW6am9CYI/FOxL16h/cBP38QPEHWOOl5jPb1M+t4+U2yGlsXUkJbPJ9AZU2x53XEApheqk8fHXbd04PuGwearoXjHxVn4VAbNYwqd3yI73CVwJoOUAnr2JXL2GN3blu62pqb2rJT+I9zSLCr9c0lb/AoyxDAablOU65WBEORxSlEPyQUlWFgyKguFwxGA0ZDgasDEZcuHGVcI8RkniT6zPtEiitSmA3d6gef1lZh+9Rz47hsbB7iHFwwcMLl5lno1xKYwuq2ZhCsAlOaXtvH7B7USKU7JiO2O2W1uhYz0h22u5gcVHH7OxmIXUS8DnOVy/jr94cWUew9Vac6Cn/KezSkNFr6qmzHOcr2lcQENtnTnLf/1P/yvy4ZCyKAJiOMtDFUubYbNA7inWxqpO4QairRTtkXO2D0xivUkpiFqUyLPPsvzBDxk9uht0maMD7L17DC9cZC6mdRSezOeQlSftv/JFtWQ0PeV1aD04KwCZ+LtVxT94QHHvDrYJTlOyQACuz7+I39qOqO+eAUfra+kuqILicc5RVRWNiyQNrTwi7vnkPg2YUiuKMUqeC2VuKcuc4bBgPBxz/UzO0CQWnXjTdsxDB1oxnhCIWYm9ehN36xb1/T2yoxlaL5GDx2R3HjC8so0tbZQ0T45Hux/jG9LdnXal/meYzifaTxEFIsHOtPMaf2eHYrqHUAOKNx4ur2OfuUkzOYsjp6VWiF9euZV2z9aKNwJfSz1fElKrw6siwtAK59dLtjdHjEYlg2HJYDCkHJaUZcF4OKQcDNp/xXDQVifa2DxDQ/nEw6apSLIAVp3IIgK5xVw6h7t0heb+HfzyEOtrWNbYvWOau58xeOECyzm9tdLtxahun2qrnePBwQEmrsOmblrS+VbW+ZjW45LDN8gaIxIDBzEgHJ2ZpQq3yoLL0ynzakETkThCcABViwVqAmdocoCmOSsHJXmeBz2jDcYTM23qYK8bWN/aDCg2Q+hfU1HVy0B7Ipa8KEP1X7q10snsMLYhFSog6TrXbviMB3xmqba3mG+sMz4+wPi6vZCta4r9Q85cvciNZ6/zaHeX2fEUrwMGo2GonhgfLNRjMJDbkC1SBLn/09rPSUQMHs/CCkaCA8eJMqtgoTAqPfNZg2scRZ4zXpuADWXlxsawXjmK2pH56Mk0wewon30GU44iPFQC3l98axpIjMQm6Ww0coVEXhsDOPUhV99arDNkFsajIb/9m3+Lmzeex0iIwKvC/ddeISsN29sXQSMzQiR1S2RyYWG6VigbCWR+ikU1Ig60R/5oDFlWMLl0haPzF/A/+Qm4JnTZenTvgGJQk8qIZ0bIspwyK7DlAFVYLhcYo5Q2i2W5QSynLk0VH0kHe1A0iez4CEcLz43c0ohnPm9AlfF4RJblCIYcYTBfBC+wBmJqI4qxyvC551FThsirQoIipnLOCojRzq6JJMIavdJewYkDI6gJJcZzK4zWz2KLEW1EUWH97DaFbTDG0veum57kMiIRuRQ3XayCZEzSSSQaqwHZYW2GGcDopWeYvv1XiPNhDkwsHakGbSoQHysiZWiWIXmGXzjc7Jiz56606XgINE0VqmWccjt//gJ5UbCslqxvbOAR5g/vs7N/wPbWWfaXM97ftSyXFeVknTvHlseLdS6PK6a3G4wPRnqC+4nJ2P761/E2a6VXOhd8nLwgCKU12pIIU/Us5nN2d3YC3C+uLRF45tnn+d1/9I/IixIkrjeFVLWoRbBBXAfaOgMSwXOLljnRr/CxkKJXt+z8hmw0ZPzG6zz66H3Eh9pHgQvLUdSe5viYcjJiaAyHvdWjBO6Hpq4YDoesra1RVUumswV7dfUF6Kk9zapv0AgxYtpTJk9+M/JQZGrI92sWP/mY3O8hElA2brske+V56tFFvNjWaGnh10m4saq4VpWymIUc/US8hyqFEYZZKKhZ4MiMp8iUoYVBrgxLy3hoGQ8LJmsF569ssL4uuBY1KPGwT0ZGq56SjCezNkJeeJ7qnXcpZwdIs8TMF5h79yj3H5Kfv4n7mRlqJyfoP4OBGO/bHevdWLZP1451l64XeLYiwqFFKii5NSw++oTx4SEpSOEna5hnb+A2Nro7tDp/z7jSVbut8SGi2HdKGGO5cPkqL37zN1g/e4mNjXXW1gZMRjmjomBUWkoDeW4oMiE3xEokvlWINcBgO2VUhSTwPIDxmAtr6AvPsrz9DtlyhtYeebBLufOA8urZUIr2xPoLMkNaJRqgreREP63mi7Uw+o6Hp72XPhEoaXrzR0Agmb0juHMb65bxRUO1tkH+4vP4ySQEKNTHPZ6cGumuHpUOHu29Ui2XkfC9G4NhOeAXfuGrPP/alyiHawwHA4aDgnJQkpUhRTjPLNYajBWsEcpIXN11Vtot0t8p0hsBXwjFi9c5Pn+FtU8eYPwcZguyB48YzKdka+O23LHX9BydEtwS8a7cWU/8PL3WOWa6CHo/CS0loXQPHvclneslXSerGurbt8ncIrxnoBkO4ZlruEkgPpUVx8LT5Lm2PaiWSxbVgrwo8FWMzBrDxvomL7/8Mo2GYI9Kwj4nazONqfTEtrY9bhMkU+hZ2pzH8L1oIDbWYM5vo5cvUz9+QKkeU1XonfuMXqk5KActWiNawLQmS3LitIdwEjZf0F78GZdtZ0h6r/QMc+Ma9M5dssOD6GwTNC/xly+jN65T53m3j+OUadrLSPv/lOTknKNaLvHRiZ6cntYY1ocFm5OCUZkzLDNGZcGgzBkOcybjAevjAePJiMnGOuO1TdbPXGO0NYk6snbj27YoCdMcR6POTM4gz73A8r2PKI/nGO/Q+SFy7w7l3jXKS1vUvu8wbJcNaUf3pVd/vGT1w19MSzdtnQ3d7ux1p33berDHFfWd+4x8qLwHUFvH8KVn8Gev4CLKJtj2SkLtRog33fqNkxxtKa/gao+vKjIbMoejGsyZgeHN5y7y2uvPMzm7zWCyxnC8Rj4MAY48K1GbISYLKCgTihyE1MYMT4a0j6qdSheDmQl9Lu09Q5/VgNmc4C9fwn1ynmbvAVYr1DnMrEJvf87whS9zKLZDqfYWTn9OT69pTL0NYAxRH0rVi2mdx96DOB+cYE3T6QQSslEya0NWSjzbKoXKZ2jT0FQ1znu8iXIPyMtBey71z1pVTzkoGGQZAR3cBe/Ue1xdo9634A7Vht0HO9y7+5hHu/scHx+xrCpMljNZm3D2wlmeeeYK6+O1kBYu/RWZ1lCnn5h0ZsQPeYFmc43F2S2aBw/I6ihz1WEaT344ZVjXDAY5Z7fXKQ+P2ds/oHY15fZ2ZG6J1Y0VjBqK3JDlgs1OpvZ27edG2iSCQiOKmMD5UanQOGVQ5DyYOmrnKcUyGE/AZHGyHdLUiHdkGmBniCCTIYNbN5AsRBDwIYyf8v/6Mpm4EJCYXyYhgu4JsSxL4M4JXBeeQVGyNlqPKRYhpcP7EPm31mMjX47XWKEpEq1qUpTVR09fdDKJxRsT0ECSFJIg7cUaTG4pshHD529y+OFH2Ebx1gTSZqeMsEi1RGxBhsGJwcZIipich48ec2ZckGcZHhdL4yp9hfA0WpfDHpTtlJ/fxMPjsPJkNuTzzRY1ijJeG5IXBXgl94ptAm9QqO4VoL7ZuCC7cqW9S4rKJ3RFJzNPSpvO4FFVmvh7Fp1sVpRiXIKk+Hy4ThFTZ0RMb4jStTtNtKf/B4eDMYj4Nm8yCVjERL4NYfLMRRbjNbJqDjiMC2zpg6IIqCoabG4RcrK8RLKM8VrOxug6F65cwpgs5nEqKr7/tKfSsixj4+wWXpUyz5mM13nnnbcYjSecPXOW2nnuznL2D44wxQiTD5g64b2di1zId6keQxbnJaEm7GSN9ZdegISDkOCYTIe6w8fUMmkjIWoEH6A6HB8dczQ9bFUeNOy56zdukuWBfb6lg5OI7JLgLGzLtkclxcf30ZC+ppJEfjQ8AaRLzwqOHktmTIAboqy99DKPshxtgsNMUqlhMTCbUqpw1mY8oGp5sIwJJOdOYTpbsqwbxsOSyXjMfDEPpeJPsQV9f1UxT68/adCkmYh7N37NOqH+dIf84AEiszBOhSI3zpBde4lGRlHX1nbMNSkUsaVIh0eo64bFLDgm0z7NBW5tWH7tjS0uXj3DYDJhNBlTjCfkwwnZYIQphth8iMmG2GyANwPI11GK3h6nhR53T9gZJCqG8uZVjq8+y/Dexxi3QOsGs3vA4OF9yu0rLMlbY/8/bvRJq421xuQXoJumZ+skWxI4T+78DrEUDS0vK/ZHXtVUH3/C2C1C941FtreRy5fxedGqoCtStLU/e7IPaKomVEEYFLhodOQGXr11jn/yd75MPtiIFQojP1uPV6FTAnU1/aVnCEiS832DR0I/dJBjnr1K/Zfn0MePwDfo4THFg3uMLj/HUkpcy7oh7dJuOVcgOO/9kwwXnRPstNrqtVbu1yeUFiJC7Snfin0S59DHj7F7jzAauEvUWNy5s4yeeYaFmHbyJK1P+ggigi4SFcfaeWbVkrw0DIaB/N6ijMqS1169ydd/+UuIFG0QQqJM7Aj+U1/jvHZitp2z8ETp/KNbtxK47uzZdcxzt1j+5McMzTxw2e3sMDw+JFs7R9U6k7p7teXQ4/pYtQm7VNnTbqtma3yI9MDtK0ENP4GJD/pllL1WwB8dMjg6xuBQ8UCG3zgD5y/ii7yLeAu9VJ30krTvhylWlvWS+XzOoloG9HncbcO1UdAhvW/XGDEk2R4FaU56ezykJCZnTeJZ6fciPWuMGmNgPEEvXaR+N2ewrFHnkJ0diukUieXnO0dCT0Jrbx+mNfSFwTM6Z8pqS71KVKJP3l9UkeND7IN7ZNU8SEsR3GgEN64hW5vdCklBotULpB5E/ShE8pdVyM01xtD4EFQejSa88tJNvvLKDc6fmTCaDBhMRgzGE8rhJAQabQ4mB7Got2gMQKcxbW+7YjSmYWgFBd5kZJeuUV06T3P/IXbmwFfo/iOKRzsMz59hJrKCtumJ6pNboL2/pnH8YrZj727S/fofaQKYRjGHM/TRfQxz4oKjGsD6yy8xHQeUTbfO48WfOOC1Rdi2YSv11JFSoxxkLOYW7xWLsjm0vPjSLb7ya7+OTK6g5IgavJgoJWPl5BUNSrqxbDesxiMh8KCk9ZQCmm2fk8gXQQYl5vw5dPsCze2PKZbHwbCvPfbRDuV0H2u3Vlb/zzi5/trNiDAsC7z32DwUpvFRTy6KHIwJrzVKnmUczRbUTd3amCkgkduMLItnvYtBeVXquqFxntLGeRIhy3NsPPd9Oj/inA4HJUVuA49s5A1Lu3VZhzRwI57DR7v84C//ig8++px7D3c4mE1DvzRUD7Z5xmR9zLXrl/j6L36N52/dxDRh/FJ6cZDfPVQ56f3o4Ffww5L51gZ1XjBYVO1sGFXy2Zz8YMbt/Uf85OFdat8EHiDvOXN2i7XCktnACZrZwBU0nx1TLBdtXYCntZ+zepQCDSIWcQaPxYln7qGuocgtx82Sqgmq2NpkhLEZzmvgctEltRWkdmAsViDbWqc4dyGW34rcMZHQJ5UKtqbHLE3EqkgnBkSVqnIYr2Qac8SsoRgUIVXFBCSNIRgteZ4HZIhkMUUrntvpzCNUsPLSf3aCwWqyeAjHhRcnMjMmlP8Wz+S5Z5kN/pTchRr1rgnM1wOr5NbiaciihzYrslB5qyg5v73FlQtnGQ0nSEwlahpHWQ5+nmn6P9ECk4EGRslQsq43w4fz4K20RjiYB2N2MCwpRkPUKZtZQbaoI8ooCElRIV8fk53Z6hT9mB/tVUNueOu8SYoiqwdzhML5pgll33ycS1xwGEUnQNjDGkidY254i5jpe2hiM9KtJSCWYw8376ZYMGIwNhjs2dY65vwW5nAHdQ7beHIPTILzyjZV8F+YsOGssWRFECblYEwiI8xCB04deprnOctqyXw25+YLL3N8cMh0NmU6n7K5sQnFJvc/3wGUrBxGfiHhw71zvJKvo8fLVnERUawKwytXyDY3w6sxbUNUkcyC10ieJRHhEQ8dMRgT0tAOD/apFstYMSsShdmCG8/cCHMeOXEwBkuvspaGQzAhQJTkYAjz0pI5RsW/5fBIJ2QyyulI2kQMo4uXyUdrNPMuBz2lZVIHgzgfjGA2jU4bQmWskOeDotRNw+Fxw7AckGdfRKnotIcEOLlIesbyCTdHezggZM5QfXKXDX+A4AIocWIpXnoWN7iIRvgwMYIbxjddI4152AsemNcVvpoyGVhmteC9wVrHpQsTvvY7v8n2S9+k0RFgA7FcdPIF5xlAKJno22foP1Pao50MSI7Y8ECKrA3guZtUb21R1PuB42q2wNy9w+D5lznKzxAYiU+ebMl6YlUESP+XL4bAtjNe+mkS/W70tLYVzVlXXhIBd3BE9ugxRhsURfMMOX8Ozp5Fje3uYPrXlt48djiCqqlomiUWh0vVDYywMS5YLwQ1jtYVmpx6dH6KttudmRPuHf9so1otKqBLt1Or2AubNOcvoTsfYbRGFjU83CGvZkhRpuBpDBwElVjbFREg0KalKAwD1K2f0239FKwwnUIL3us06xP37hnI8fvWK35nD1stu32VZeTP3kDXNwkJFJ2R0VF5dXs7jIdHyWi843ix4Hg2Z7Gs4ihbyqJgbTRkmOWhvH16hri3k6wMDu7+vo9zFj/bOsxEkZ4LHdojGZ9nlC89y/EfrjFq9lDv8QfHZAf7mEsVKsXqPm5XoHajp9p7DzoaydNs/R0nvR7pUz6jvZdl5SWJOh2PdymmU6xGI00MnN3Cb66HNGyJ6WoRKdwFG5+8swB1VbFcLkPkuteP9fU1vI+phwS0mZXk+knzsCr7lXQWh5dMmuBWx1ptCdFKXsCZs7jBBL9chNmZTZH9PbKzW/F6SbPtnHGtryjJmagHSyeITqf9lP637/V+ObmiIBYc2N3D7u62wWG1Gbqxhbl+jTpyZaysiIQMTjph2idxc3rvaOoGEzkwm2UNYhiM17n20hu88stfZ32y1vZJEoJO44nT4zdppWQbEOtkd9iQCRcSf28ReAJra3DzBst3PyRf1MEpvJhi7j9i+Ox1smFB0/u49uax/zMtk24Mnub+OoXWOhrTiD9Ndp84K0WxCLKo0d0D8tkhIpFXTzx6cYxefAafj1kNYAhIl5HR3alLC09NFZaNUjlPhkE12ZiKzQ1mUEI+opEBJHkdv96s6BCrcoNWb4WVCW/3ZfhwSsxLc5CqFHtr0DMj/LmzuMkmfvkIi0OdYveOkZ17FJe2aLSXxrgypqcrT41EdKa1hKq4ofd5njEcDRiOhqjz1MuGxaKmsJa9w2OqiAq11jIYDhiPBqFwi/fU83mgNBBYNg21dygBdUqUob5HzZCG0aHkgyGmLECiDRsL3HkNCHmvDZ+9+wH/2//0h3x6f4fDxZzaN9SRHBo6Tfro6JCHDx/z4Sef8Tt/73f4hddfw7igjifbAyKHZ9J5YAWR44ylObNBPRjA7BgaReJ6sYuG4bRm75MHfPftd5jiqdXjvKcs7zHOc7yryfIMNJ35IFkAB/y09nNaIYJoRqaQq1AZTWgglo0yHhh8A1XVIKKsDUdkxmOamnVV8toH8i0RGvFYhPHZLfK1Sbul2v9rtPGNgJGQMUVPYTNg1EfeEEFdDV7JhOCksYbRsCTPixA9TLm6KGJivZPWAeCS7YKYAEfue/J9vCaGWJZbaHn5NZSj9hgsgvMwOLeNOXMGO59jvcN5h5GMbLSGKcfQLBCBPLeUeUFmDDaD8XjE2voZiryIjimhEA3VeU6xmTiOoiBGo2fetMTBR5VijDAuLAezBu8dwzJnUAzB1chsiiyWGOewNniRxQrFxQvIYEJn0CcjSTqJJ73fldXDOSk8CeHkA+eRtRmDbIARoVZl52DBzu4h+3sHVPWU4WTE+a1Nzm+doYwETm1iSUIUxQPQSgZZgZEuXalVKEWCs8xYslFOduki/oMPMBryDvGOnWnFxJbgjxHfhBLuxpLbEEnBO/I8D9B/hS7l75SPRBF2d3d44eXXuHDlOu/88H+mNJbh2oTd4znZ+jobayX1YoixGcmFtr8c8FefXuSl+jG94cGLML55C1MMOsMoKfTaUxIlRuPD5uyURhH29w9wLpA1eg3zXIxGXL52DYNFjUZkW5C03iTxBilNpOU8ifs/ScfgdJPYYY9P/ATtuZiUm6gwi5Cf2SA/dw6/+xBEydS0DkqjSjGbc25Y8NJz5/jwJw9ZLKOR7B3GhOpgaU3Ol8ufrkj+taYx7Yv/2GHbO5hbQz8YFvnM4R88wPgjIJRB99sjiude5khGrYrfv0PfuErw/mRsLyrHdDZn4RwNJspiQ1mWSLlF5TdxlJ0C6KPxJSAkyGrfybT6r2/O0Ys6JcW1MZ7h89eYrV1gfPQ5UKGNg0e7lMf72DMbHYT/hOrZGok9HWbV2O+N+ak2Xfm1M8RX+5ccEqp9h1w3u5kIfu+QcjqLiA5FyxI5fw4dT1CTGJ56nEA9Dp0+klHEBOLTqmJZLSMaLXwoL4tw2OGirK1AIzmmzcDkKDmo7ZkyPQdcUvrTs6YofJLh6efaELl8ifq9AcZNAw5994hsOsXkm908JicxJ82HzinS2iJy8r3TaP1rpTWc9luazG5RtXOblPU4GEag8ErzeAfrAskl1jAvc9aev0UlveofPQdQMoLbvmiQrwL4RqlmNa5yIUNNQ2WgsigZDAbgE/LQh8oTCUOakHKtEpqu3TF4JIRxID+O93za/hDIzp3haHMb9/g+RucwX5LtHVI2ylFm4vz4bg3E+fReUdPiL3t23BfhQF01ALXnaej2Rv9n/3fpg1hCIYGDQ8xyDr5B1OCyHNnaxk5GNOqCLtU6rbtdmdZFsqnSM1dVxXw2jzpmOq9gNB7i1LM8nrN/fMT9B4842D1EvWc8GXDuwjm2t88zGo2CPiiQ1mlfbw5pC60FGfRYSSNvopDJYHMdPxmjh48QPKZp0L1DchGkF5TR1Pl2YFJqaye3Tx0xFQfupHoIxL51byTdrZtBsOrIDg4wR4fxVYNmJX77Avb8RRpOcuTF+z1xBnd70nvPsqqpGx9L/4Y+GFuQlSPUDmiis7MlOtXVVSbdZVdOxK4LveeI8ibJmfRZb3LyazeZrU0Y7R+HyqbNkubxI4azOfkoZ6nJb3HibExn/AkkSkrPPeVZbJ/15L2eRLr2Zi92Oc9yzHJJ8/iAgS4g4u9rUzN68XkW5RkaKfCYlfWS9nDnDlkdZU371StVVbM8nuLnx6gPiEgxkOWWLM8Qm4XrndC7NJajPWnKpGdQ6Lja4r70bWZDfEST9s3qAvd4zNoAv71FvX4Gt1tgtQIvyOEc8/gh5ZUXmYuNfGHtyJ3+PiTyfGaW0aBgdjxl72hGDWCEfLZgOFxw/fJ5NjcnVMuGQVmAwt50ihdhNCzZOrPOoMwRDVyJjYb0NO8cTV3RNA3ed2njzkd53XucCEYmL0pKm2E0UKIEWQSigl8uee8vf8QP//B7fPJ4n6WvcD59ppOJPsl572mqBY/vVfzb/+XfMRyNeO3ms2TxvAxxZo3yu48U79ImvSq6vkZVDgLoJN1HFZqKolkyFMNyOufYOzS3qBjqes7cVmRWkNmS+XxB7V3IbBD5mY7wnzt0nHkhJ5RcUwmeN+ehqj2jHJrGM68CF0IxHgU0SlNj5nPyeknulVxDGV/KgvzCJSTLo6GXog9JMNswWUJwsLQnIHECwuZTEVzj8Opw2gA5TjyaCTbPEBTXOObzJdPplKPpAU1dMRlvsL62xnA8DClPGgmGRLvDPg6gAMZmAWURI+5GIj+Hi544Gxwt5WhEsX0O7t2DmIbT0LDvGs5nNqA0aDBisdEbGUpO20Agm5WID6kcmTWnGskQ4kI0QhZ5CYIu4+NGgKNlQMcUuedw5lDnKXNLkQ3i0R8gSRawjmDcZhmDm1cxeapQE+8XS1h2XrhOSKVy7itNw0ZoCNWtjDFk1oLJ+NHHj/jOd97jJ+++w/Rwh6ZZotLgXU05HHL50jXe/MrrfPmNlzmzPukuGJXgZLD76KAxSAoVR+MRrA1OnzzLKK5dY4FBnEPwWOeolpYmcvgIHmNsLIMnGJMRQGQGrGW5qME7jLUnIHZ//VbXNcVoyKVrN/nRd7/LbHrE1tmz5PmAz6shDz5+wCvPXiGbGnaOlySSWedh/7EFH6qG2aQQGMPwmeu00VLvgxCyBtKnfJrDdPB7vDrwgaz24YOHuFQOPA799oULbG5uMJsec/vz27z/4Qc8unePZVWR5TkbGxtcunaNW889y/mzZ8ni3mqj7tI7crVnLBHWcZuCF++XBCqq2LygvHCR+r13UB8qsqEGL2C9J5vOODu0vDtvQmUVs6BZ1LFyWVRK+8iDdJNTbT2tsf27d5i3mOkThmz8eC6W6v4uxXQfNJRe9BnIlXO49at4LVqJmkyL9Dwdzkxaw0IRFpVjPptRz2cQydWMwKDMyYZjHDaktUQDVGIFAZNM8PaRkoKWTMTOSEn/kgHQV65UlWJ7g4PL1/D3foz1FTQOOZqR7z0i37xCTdbrf3eHtE5WO5Jek9XXTrGdRBP1DeP2pfiYK+Wi2++H1yyC7B+RReJTJEOHowDpLyIPQ8+y7FJg0ppIFmJYt8u6YVlVqPdtJ0WUciDg58z27rF771MO9h5QL6ZYVcrhgM1z59i4dINy/TLIGInIqY43IN0r9qc/1HQKjpQ5enaDphhSzGO1m2mFnc6wm0qd0Baavttpoi1kX9I7ybEjnR16qm0VERn07pTQ+aT51RLaak9hVkFmC9jfwzRB3ohadLxGcfkSy+So6W/7aGCtRuLTMwvONyxmR7h6ToACN4ClHFjKYUbtKxazBfPZEctI/J1ZS1mWDMYTinIUqoF1WfkkJGir0KLt83Tusa6rKgKTEnv5Au7Ru/GMUNz+PkWzxOTDkNme5FWcp3YndlO8grQ7fXkaOi3xGTuJc5I7pLeH4v+6nsSd2zTIdAp1RUrs9VmObqxBURCQhbIiv9KSkN6lendhsayZL+YhUNiTi3VT8Zff/T7f+fff5f0P3md6fIy4aBgaGIxHPPv8c3zjF77Giy+9yMbmBmI7Sart/TUYstHY7MsZTSUaRJDxCB2PQ4q+KsY7ODykjCWo27GIunDfOA2aj7TPedo+cKXnvtCn36AHsH1iZ0rTYA6OsMuaVHXG5yVcvIAfB3TGk6l6/fuf+F0CyWq1rALfXeLtELA2o8jKgCRH8BLoEHwUkxL5wGzEo/Yfa/WE0NYBHnok9NdxErOKITt7juXZLfzdh2TOob5Bj/bJjqZkZ9dCoDtef1WendhrvW3+RezCp125j/x5Muk1udKEwmaYec3y4SMmuoCIIK4tjG89x8xO8CTuxag3xO929+30gN6j4lFqjXZqE8iNJVYIEg8DK5R5hhpLg6HGsnQwn9d458kzYVRYBrkJXG+aqBakrb2macKgXWvJYRMHgpOHTUozlkGOnFnHbZxB7RBpjsO+rjx2d5+iXmIYt0jm9v9fUFDR5gXFoGQ6XTCd11TReWUXFXXj+PT2QzY31ji7tcHmuS28QvOgofLK5uaEzfUhmQhN7ThcLJjPKxalpXZKU9c456MDqgvWpnXS7Y0wrkWRUxY5FsOi3S9hnT/8/D7/5pN7zI7m1L6OTtMwJibuRy+9s7cVasLjhzv82Z/8Oee3tri0fiZ8pxeUlvT52K8W+ADIaMSiLPAiHZ+qgDSefL5ka3ON9cmYo6NDnHOIiRWhPWAznPORyFlDQaWqwvufviN/TqeN4o1DVbA2SCSnysLDrFY2h0KllumyQY0wzHNcVWFMKCtqVMm8i4gOiysN9sY1jCkAablijBAYpyUgWIx2hGzpM+2MSvCcNt4Fz5tPg2oYFgOquubjTz/jnffe54P33+Phg0fM6zmNKpkYzq6v8dxzz/PG629w/do1isGABNs1EFE4Gh0nhlRRJ9w1pGRIjMpba8FAVhSUly8wfysoyOI9xjccLeecw0JmMHhMliGFRY0lz0u8C5AyU+RoXQfSsx5D9mk1HyNwLkHaY4qTEROI9nxgVh+WcLQIsNAsG9B4pa5rxFicElJJRMEbtFEgx5hkTHULPDmd+hCz9JGVqGrcrI1TxFqEGu8bamf5l//rn3D3zj6L4x3QwEEiVsis0NQNs9k+u4/u8/aPvs8fX7/O7/693+aNV1+OvDhRsVYfEVNBE0r560HuRwXJWvChClG5fZYZBnU13iulLdhyUC1rykGGqMOKoczyOPcGrcK1jWSoW3K8v8d8uexK4Z1SC4dQzicffcSH77/DpYsXsDbnwGU8OpjjnfLZwzkvXNnCqXIwXbaQ3bwOnAhGAzpFVDBFSXn+fBgPkda/1s1ZEHbJhEm59KKKqsd7x/3791HVtjSeAOPRiP/xX/4PvPPW2+w9uh8dbREKqGHdqRjGkzVefO0Nfu1v/RbP3bpFllnQAEtOHviQKiexD3FNKaRgS4BuJs4GRawwuHCBYw2RMk+IwlkCKaeKMjYFj24fsVTPeG1EZRYsq45iOaV/GAjOyy8EpZGa9H6ualNP3ja8kUvG/M4Ow3oPYnUFl0N59TK12Yg9D19uObKkS4fs3QIhIAVn85pqPkd9TUJ1GKAYDVkONri7yHl84JkeL/DLhszCqLRsreecXy8YZrEyhva7qiuP9KQZ1Zl4CmhusM9cpfrehJz9oPAslsjuDuWthpl0ZNkrw9czfJ9sX5xq+sRt2gkL9+yL8U5nkPYTrVO3cejRIVIvW7isDgp0fYLPbHRUxj3Yu17rEFMgyjmPhGo1i0VbhSGkwDjcfJfv//Hv8f3vvcuduw84mM5oao9BGBSW89sjXn3xKm989U3O3/oypryIUBB2d3L0J2UxPqlIVH4760MtsDHBlSOYm5DqtnSBXFpjv09OywryC5JTKKn0icj8dFu3KvsOi7YXQvf6U4wBaT1y4I9n2NmC5Cj0Apxdh9E47iQ6hZ70sZ5xnebKKxjBN57FbEldB/g3KEYcRhr2du5z794jPv7gY+7c+YyDgz180zAoMs6eWefGM1d58aWXuPrcSxRr2xjJSajEFAyWFQtWEjZ25VlFBIqM8vI2zQ8DJ594pd4/oGgqUkF4Egqh+ybQofm6kT6Z7nmarT+XuvJ6eOeEdIgfbU33RHzd1JjlEvGBx0EkQ4oBjCao2IB4inpOC5nXJF+T67KHllColg31suotIcV7x1/94K/402//BxbzWNY49dEDXqmOKn70wx9x5/PbvPnmG3zrW9/g6o0bWJOtOCDC0Rd0c2kdhOEB+3LCFCVuMMQl40M9slhgvQs6UHwUHx1ffax3W7hANCLsTo7zX7MJJ2eo9548cas+UhMFWdbo3iG2SXBng8uHZFcuUZm47vpeRJH+19szSdAY4Q+VJeuqwruYRh2dnibLyMqShWY83Kt5tDPj4GjBsg56S17kbKwPuXhmxPlJzsA4THLYidCSV6TtkYJE8X0vxDGWznAsh3DpMs2PP6SoagSPqWZweEDmLyAmcH38tEEUEYiExSvq9xep2jy9Jzxt3QhgvCDHS/zeAUIV31D8yGDPXcGbspWhofXk9olXVz4RP9Z4ZTZd4usqnJe9wzkrS5b5mJ8cZfz47j6ff/qYwwc7zI/2wFUUGayPCs6dXefm9fM8c/08W5vrLWVHdy15gv9M5enjHOY+ABDIDEzGsLmNy9eh2Ue1Dn6r4ylmOUOKIT2GyC+sWSPkmWBtjs1yHAFcAYAP/Iez2QLXeJxTzm6dYTQasLk2ZlbXFEbwjcNZ4ejwmL2DKZXzVBpSonzThBLiBI4oVcV6E86nODAa7QQAsYaiKMKTq0EjnyrA/u4BtGmJFhGLjRy8IR21icnX2qZ3JzSrNg23f/IZ737wEVtf/jKFMahL2TQ9HSvqsv2lJ4MBy+GIjrk06D9GwSxrzownrA2HDOsFtYYslhRAd84jJqSQFc4xUrDTKUfzxU+dk5+zepSgmtFIQGUERTR4o6oG8sxiVJktKlSVsiworCX3ngyLikVpImzX4OoGsiIgIAREbdDyxMZ8YWJeb6fMp4hfgCt1BkjjAjJAvWAlpAjs7u7zP/4vv8e7773Hzu5OKFXswZtgnKtz7Dx4yPvvf8Qf/NEf88pLL/J3fue3uHHjZhSYPnragvAOy8oE4sZkCMXDWY2BLMc3ijdKefEcUxy2aRCFYQ5jShazGeOywKsnN0KRlSgSSoDFw95mFvUNi+k8zP8pk5/mPekRkWghoBgti6UTnBPKwvJ46llWDaOBZfPsJqpC08RDJ2rPIiA+IC68SOAgiqFQida/EDOEJOnkHYAxMfG3M+0UdQ1eG0Sgrjw//uH3Yzn4IDSMgsWgYiHlg+Lw9YJPPnqX/9d/94C/9w/+Lr/yi98KqWz9wz6mxYVF1caPA/GVhnUEnmJzDTNcg8V+YE1f1GzNPQezKbI2RownyzNMFtnRrcHYEA23JiCMdvf2+IM//jaJXvnUmipvvvlV9g8OGBQlRTFg3sDDRcYkq2nKEbVaPrx3wK2LYwoDj6c1qFA0FSZlrSUy4KLATib4pgnPk1nEy4pyncZMPBgf0+qEAPdrKh49ekCK5hErDv34hz/k7R/8IM5/Wg/xuJGA2mpcw9H+Lt/90z/inR/+gG/9+m/wu3/3v2QyHHZpZTF9KjmEOq6G8HbbS4n3iWOUnTuLj2s7CPpodnpPvVwyLjJsFMLT48B9I9bQWjQpKqzKC6MJ7+/vn+40pkdoQ2zdTug59pOUWUF0BOUGdHcX8UeAC3M5ELILF5lTxM9p5PLp7me0S6FopasGaOpstqRZJJRNiEI5LPeaIb//Xs17n33I7u37LA8eo9UsVLwblGxurHPjmfP8wpu3uHnlDEZ8Or96ypNGk6QfZ9TeExHn2TO8dpEZa4zEhmerK/RwH+NrNBQVf3Iwf0r7ohXSNFcJRXDydq1B8BSUTfIHGMA0DTILqaeBkB8YDmA4DHwaPcdb+mI7jpLO6A690lQNrmpWymMuK8e3//Qt5rMFe9MKl/g40rrD8/n+AR/dOeb9D+7wK7/0GS998zfJN26F8zmtxb5vI7ZufXbpUjIe4gfDuAobaBp0sSDEJS3dES90vSSCMxMUOZnBX9xEds6Lzt1AhPMnxFh/7lqHS+8sMwh+tiCr3UoOvDl/jsbmcXt3kf0VtFtP2fctybpnXjccLxc0viZd1Kvy4NEu//r3fp97D/bY3z/EE6ropfuaTx/wg7c+4vp33+KrX32VL3/rW1y4+jw2ckH0nzWtlxTA6dYS3fvWkJ3ZoDYZw+C1x8/mZG0ucH/c0tfi/l6ZtnA+yBdidPRGNnWk07WTsF35bOD7SpPVk1R1jamWAfWMBAh+nqODgl7B5hMP/JT+CK2TbFlXVFUirOxk/YOHD0B8Ty/pby4BD85XPH70kD/5kz/j8eMd/s7f+dvcvHUrVlhc6Uxn4PfHJHKnqdGA2is6UnOPR5slxrtwvdaGXcVDpB1qEpnEimw/rSZx33VzsiIzk6yNE7u6JxVZVujBfuxjQEI1gxHF9nbQWbRVf9sjPkntZJy1oxgXTir57eNaT3esgc/3Zux87yd8/JO73L99n6PDfZqmwoghL4dMzpzl2jPXefnFm7x4bZNzY0suvpfq1QmKpAv4tE61t1LiTZ21FBcusRTDMO53aWr04JDcK5ieo6ATKb20IQLP4sqQ9+TPKbV+CvAq4qubu/bWdFtTCHuP+RSzPAZC2joCrI/wo01UAgFxf4Wb9soSMyV6eyHt9bh2msYzn80RV+MjsZoQkBi3Zxn/23fvM/vht/n84z3mu3eo5zuIn4UMCQRjM0bDAVtbm9y6cY0vvfEab3z5NYrRoLNrAgcHratYelK3XXOsPANEKoBBDmtjfDaMzxOXyXyBLGeY/Az9tOju+DjdORQJWQdZltIuW4UUVagb11JN7O0dYK1lY1AyHpbBpvVKPa+Z1hUPH+8zqxsEaNTjXYOvg9OmRb+phlQpo+1u7C0VVAxFWQRE20pHQ6fCCsgoh5tsnd1iY3NCs1ywc/8ex9NDGg2VTDukbpSJXpkeHvPhTz7mpRee5+L6Rit7e8dIT/bQ6mxqDM2oRK2FRuLZGVtdMRmss7E2YlRPcZkli6nE6hXvoXae2oUy6kWRs7Ex4c69Bzw+eHrF4Z87PcpGdEKjgXvAolgvVFWDyXKaXJkug8IyKAfk5YClb6hwncolEDgmlMJaEMGmg6ePIdOYepMWeDTgUlRSejx2VdPQ4IIDyDuMUR4/eMy3H/0RTWvIKT6iOFKFdofivOPw8IA//4u/4PYnn/BP/ut/xBtvfKXNLfN4jCcQVlkDVlriqC7CpqiG6IZXId86h1KAW4B4sqpm3TkWsyMmkw1UPNZkGLExfV3IsozMGDLJUeNQ1/DZJ58wm89+3mn6mU2QSM67qscE5VRYeMuiFkaDjKNlzbJ2TIDRcIzzoBrSavDd4ScpKtxz0ghBWJtWKkeYbTRsIkfcitASBeMdxnucb8KKiUacEYP4IAjCtWk3nBFiNExRdeztPuZf/Yt/xajI+NrXvh5QRNGhpMbS3hxt++nbfgQnnZkM0NEEdgRjBdnYZFR5DhZ1SO1yDWoCZwESWMBNzDeXmHa1XFS89c67zGcpv/p0mrWWfDjk0x98j83NDRZVw/16SGWGrG+eoW4WTGth4eDhzpQLZ9coRgPuPTyicHUg/k5zYoSiKKDI8QFshWsarM3w6rFpbJKSFv+l6lgA89mU/f29djKNhrUQytQSFPQYdQjRh+iUNaEKXBCEntnRAf/H/+9/4uhwn//Lf/PPmIwnMSAV5ilL1rEJSKGVgwtCBEmSbBCyyTpIpDLVRC4enJV+OWcwKMgMLUIvOUfCmjCgHvGe5zY3eeXMWd67d+dU5zENa+jV6ivJIOxap1i1+6Z2mOMDjF+ChJxsX2bI+haNZO1hn/bLyiV7doMS0FO1V45nC7RahJKiMeIxd8r3333ADz//X5nuz/DzXdAZaMoFt9y1BR//aJ33vvcMv/Zrv8Q3v/Eqg9wGfiNC9ZKU2vOzVIuEpxpsb/M4W0erDLRBnaLzJVlTI5msXOVpJlNfDe3GchUyfupNuudbuY3GOWsdiunzvdlXRSuHLCrEa9JE0SJDi7xlWfORZ6ifs5BwSoF3Kt1TaZqK2lV0kX+oHXz6cBoTxfNoI6VzNvznvPJw5tj/aJ8HB3/O79YVX/4b/yVmcg1MRsq2EqV1nvoYvQ6zLe1ak0GOy4vgojE+oEzrGkMs85kMezRu3E7NTokeybUexi8iN065PbkPe6+2Vt6qip3QfT6ONwp+vsBEhydALYZiazOWle3u0k1hbyOmd5OBBixcSKnxrmkjt87A44MQufSq7dnVuwIemDXKB3d2eLT/59y7/4C/9Vu/wY2XvgzZON6nM6zSwuzO5D7/lKBWYDKksbYdD20axNUksnjSOEDrcAsGS49TimS8RAXilFvbZ33ytb6M1e5Xep2nRc/ULqRlanByiwDGQpa3+yR8JT5nGri0LVWiIzB8zqujXi7asrirThWJulLg8Mvi2ZvSB9KtvPcsZjPeefvHaKP8g3/w97n2zPWYohVlT3QKGnTVUG8NWQmoYNur0qqg8VlTUC0ERrpRW1ml/e13ylvxJHLoqSukJ9xX9q0qUs3RWUgpQQQVixsNYTx6Yv+lw6/bh12YSlJfVHFNQ1UHjox0V1XH/sEe3/nz77CY1Rw9vA/1MQEZkqJiGQefDXjw4TaffPAin735Ol9+9RlunBsxzk0MJHVIj9S7xOHZvtbysgaHRHFmi6XNIm24gnqa40CYnVLa+uu6TWuVoC8nW6obztOXp/2rP/33rn8n/9TKI7MmOL/xEfKs2PUxLkvOxv7q6HT5BCjoIx+k/Uw4PyuvHM2XwdEdA7ci4FS4+/CA+wc/otF3qebLYMNpg+JQdSEIWBuayjCbPmb38R0+/eQDPnz/bX7rt3+dC5evUrfC3bdItiTwlU7stXvq5LAUGYxG1KZEidyrCFotoV4gEtzGrV6jrRT5+abmP9oEk+VkmUVi1klaRyiog0YcSarP5wvWihxrDFaExnmWywV7h8ccLZY4DUCM2nnmi4rlosI1DvUuBOAhrN92RntrN+7NcjQIgdWm+1wiDEeEbDTh1/7Or/PmyzeYDEvcsuLd77/DH//hv+fh4V6wJXOLzSz1siEhF+q65sGd++zs7nF+bT3wWnraUuRJJnZnSwwAG5BBcCSt6Ogo2lQMyoyzZ9d5WB8jwwFFnlNVFTs7+zQxJcohFGXB+tqE9dGIwmY8Pnj3qTPy83PaqGMZ5YGNP52zLCobeE9UmM9dLOMVymO7JvJjqGKwYSDC0MV0mvCIIv20hIBUSALU49vF29ueQCAVdbUHHwSS0wBp8ggOTxYP1WCAhiLhFQ1o4mVwgQAS5c79e/zzf/7fk4nltde/FJQNI4iJOW5IgMWqBOdBG+6PqTYSDFm7NkbKAW56FN6TnMHSsZwvsTbDSKhSY7MCpw4jltwEyG1yYqkq73/6Ew4OT9fgVwm52KIBldQilyWo9rWDeaOsFUJdw6JyiHoygdo1OMJcioYxEQ0OFZwiXgPPkEnSN8VIDQnmnvSLtPe7RR42Rx1RHAp4FzyxGMNABmyOBlw4u8l4bRz4d6YLHu7ssqwWwdCJ0yEohwcH/Kt/+T9z7eplLl++FhUl01IwqPYEal9pSpHQ0uLKEusatLT4rQ3M3cforEEkEMChDmMCukZiuWgTViq5tZRlwXS2YHdn91Tn0DnPf/j2HzAahBTAvUqZlWcw1jLzwmQ4ZOYaPMLOUpGDJc9dP8f+zpTC+4iHCVF1YzKkKEOZchPWX1gP0qFOvA8OVA8pv7Q9HlQ5PjhkNp12gnYl+mViZGLCcDBAvbJYTKmrOSIBDerx7cHjXcNf/vEfsnFmk3/4D/8JhckQBKcuVJOxtkUKGdHuftIdWQlgnpV5VGIDMidLUSwVvItOZ2zUCbTNrSUuS/GOGwgvieBsTfWFGPy9qPBT3gs/kpEn7UmvCrpoMPMlpp1RkEGJFiEVo6NT713fJwM4vNO5P0xIj5oucU2FkngXYiRibxf2DkPFNpqubwh4pWkMh9Uu86MH7D/8lIPHv8rf+o1fZTQZgnbO9nTwnTQF+kaU4rFrGc0wR6swP0YVU1WIW6JtdlRnrrbqW18D0l4fv0DFdMXuXelHh47q0Brpgz0TUiOnQe2CV0VBTEBtah6MrL5hmfRxkc5J01PxCco91FWDb1yrZIV3DEqGyUpsPsYWo5Dy6h1NNcUtDxGtQJW6Vn7ysOJ/+Tc/IM9KXvuV/4JseAmvoYqVCu0aa9kCJUU7w+8+U7AJZhzPgSZUe4zgoV6KUF9udM8Sk5PDatWewXyaTVdM+Q7p1jcitTeU2q3iNi6hPqCIXMJiRH64yYhQv6LVrleu13ZgxQgJrzXLhmZRxYHu7RoNHBqS/iWElXjAQUSNKrA7XfAXP/yQunL8bl5y+fnXEVO2TqBW+V2582rzgBRZqHDSBKA5zocSrr0IbL+1zppkhKrE/iUZdPrz2O2Sbm5+mthuSwFLf1x5ApkYGKAl+hTjvo7X6KcHJTne6lR0a0W9Y7lcBieM9KShgBHLZDzixo0bXLp4keF4TOUaHj16xMcffcTuzk7Lc6CqVMuKd997jz/4/d/nH/7jf8DaxnqXVKXpuikEEechOSiSBt0OfQynxofvEFa9tdhDgyRtzRjhCyGTlu6XsB47o+1n2qRRlzN1BVXVnanGosMBlL3y0JrU0f75AK0SEu+bzlvXNCyqZUQFhC+rd8z3H7M42Au8fto/E9NvNTQL6sNj7r+3w/HuXfZ3vsK3fuF1Xry2yVreQ9esrNwgfEQS6oZ4hoaWTdaZ59F5EZ9b53OyFgZwYl8FJalblz3ZtTrmp9d+2iX7GgeSUgm7ZhB02cByiSFV/VFUPTIa4k0WbLd0k97zts4c1SgTafWcrimNV+azamXvogHlslwsodoJGqOPcrwNLCZKAIdTCfZJXTNfLDieHuKrQ3737/99ts5diY6jfh97XU4da/vd7UsAk1m0LPHZgC6VVkOQrqmx6MnLnnjG02nWWgZZFgj2e1WOoJPtLpZON8awrCrmywU4h7qQDrp3NOVwsaTxUbYI1N4zmy+o5nOqusZ5xdvOp9A+kw/zE0RTWAnlaERmsqjHaLcWCPN//eUb/PKvfonz5SBwBIqwNR6zPJryh3/650zjNjUaHNttQEjheO+Q+w8f8ezVq4zE4AnlpKSXrdEGHeJe8iimDMxVxDOuLWTUOHIRRuMxm6MxMhlSDHKmB8d8tHvI3IdNndngimmaUKn52rWLfOftU3DaKNBIGFyjsbSlBAX7uPJkIhRq2D+u8b4msyEf22lA5rSKqYQJtxrCdUnwhBQpWakcE0pyR0UvTZ6ASlTk4oZsqiW4IPY84JtIuuaD4Tkaj9jYWEclw0nNfDrjeBY8f2oFdWGBqIGHj/f5//yLf8H//ew2Vy5fDge4WIzNEG8jP4BEx5BHxbQBQiF4ErUsMIVFvENGI+TcNhztYOeEkoS+QqkDVKoOecTeBLbyYH8ajDHMlxVV9XSY1H9KC3MIVjslWKIwSoLEqXK89IxHQuNhNg+pPee2NsiyPLDnq4+GlIfI0O2XvX629qG2gjMdiF7D3Bhj28+l2TWZMFkbdu8rqHOIKK/cusjf/p3f4tmXXqMYjlEPR4dH/MV3fsC//jf/O1W9F3g7Iu2RGs+DRw/5sz/9Nv/oH/3TOEHxX4oURotBiR5VMbRloY0JcGgBnEP29gNEkMDNgIJogzF5e5omnhxQrM1CeXlrW0TKabXJZMz29jl2d3Y5nC5YbN7E2hKHCQ5TKcitwzfgEB4cN9gHR7x6a5vBJ6EijI9WidaOPK79aNFjxIaodtyjKSIuBOEZBHaAOIKyv7ePd7GahvSMAGN58dUv8ct/49e4detZRsMhinK0u8f3vvPn/Nkf/T7HxwcYlcgaH+RE7Rx/+ge/zze/+YvcvPpM3O9hn3low/s+zlbPZA/Dn+ZHEsLH4yQI+KQsC4pmFmdiDrWx2PjZoGR7rpmcF52SzSvuuZr6CVPmr9niQCXjIT2D9KIN8aWebpLUZsVVDaL1qhKW5agtSH5TkvHZDVs0fJMS3MX0ag+z2Rzv62hc9Yxo7xDxIIqNSryLOmI4VEP+aVMfs/O44k+//QesTyzf+MVvUQ6HK8/ne3PWGnOs6jfGZmi5Fg/lcBfxDcY3wVF/QuFOhJk/e6vpibE+pdYaNV1/Vt7T7mn789rq2HTjCO1w0BlXPWU8yczWQpNuffTuoqosaxec3mkvIAgZebnB5sUbXLrxIlsXL1MMBizrhnt373Pn/beZPngf3AHg8B4+fVzxr//tD1jfvsKtNzbBjlvVW+Og91MpwnroPBOdCRzebW09TQZzMhR7h3/PsZHWaYfW+ALsDG1vE1tcT+jKour3J7zQrV2DxsIE4YJC1HXyLNB095yu9C7xZF86xbBaVtTVEujSZwSDlYwiHzOebLK5fY7JRiCB3DvY5/HDu1RHO+DmQbFWOFo6vvf2x6xP/h1/b+ss4+1neo6e1Jme8O51Lxkr7XikB/BCEAnaX7TdnMbhUYgkjb3NqV+I6y1deuVeysoUxiftrSLtXm8/Y0zgwEuIIfHgm5C6SChBm/yUaa67wktREPUu3zQ+GIQajQDCOOWZ5fLlK3zzF7/J62+8yrnz57FZjvewt7/Pj374I/7kj7/N7c8+xTV1FAWeZVXxzo/f5ZV33+Wr3/hqeKJ2KtN8JD1nZXSCzuaawGUT9WmxWRvgQBKKnM5xGfV2hSfXwSm2/h4/8c5TX115RRXqGonVgBQfnFdZHugZUufTeqb3N61WQCeng67bNA115JnseqExhbgOqGHRYEzHqXcqPXRdDfU+0wfv8953l1jvKc2XeP7KBqOsu//Ko7SyJz5lu5YEU5Y0Nj4PMYhc1WTe09XklFaUhj/jfGk8K+MIpUP39E/F6AxcnaD4DukAo+/AgTD/vmkwTR34f2JWg4oieawYFa/SF6NpvEwrisJZ0j+T0s/Ge5aLGRJriaVhSmMi6gIiVFoKTCDwkLQcsaqo+PBateDxTs0PfvQ222fP8Gu//puMN85GuZf2Yc/Re1IH6Z/lBNkjeY4XG4/9MEHiHbgmzGHSyaX//dNtxgjra6PgyDZZ0MmaOgSnbUYaY40jW1U1R7M5eeS7XVY108UyItQ6qauAOocullTLJY33FIEsK3xKu9nVJEs1AAImozFDM+BYXNDdJfDKihi8UV588VkmuW15m9QI44113vzq69y7c4+7e4/ZOThguazaoU/Dt1wsePjoMcumYVyWGA2ZQH1qhtipDjUtGlLX4+f6Ojo+yNEsyygGBXZYMh6PMHOHrx2NCFYE5zyLxZJHj3cZjgsunj/zU+fk5yYiTiOehIXxgT/moA7KfJEbjpY+RLHznM2z58FYrATzyijROeIwrsFVywCblqiIxONMwiqJx0I0qr3EEuCRICgKBAFyE8hFnRWMc6ivcU4ZDUtef+1FvvHNX+LWs7eweUHT1Dy6/4B/+0d/xI//6m3qRlANaRxGAhHv7bsP+JNv/xH/5B//EzA2Om6iw8MI4Gli7wyhyo4zMf1KJaTPRJSCcQ5rFNtUiA8RzMyHUujGDKK2J5Gs1oUhNoJkhrIo2kPstJr3MZPZhMPcxGlNi1BVmc49Wxs5tReO5yGSdubMOqPBJHDi9KLIasKmaRaLTlGJh1UoIZtmMd2jpxwitKUWRchyy5WL2/zk3REiFZkozlnOnR3x3/63/5Crz30FicTVqkoxHPObv/0bIDn/3//hX7Gs58EjLz4oGN7xzjs/5rd/e5/x+AxGBGsyQvWGsL6cpjkNSBuRLCA41OKJpfVqj+7u466cYR+4GAmk0LrlsTFxA6K01cAya8myjNOdQSjKkq9//Ru8/fY7/NVnj2MVtUSuLMwrzyjPWFZ1O+b3d4/J1PGV9RHNY0A1eqJDDilCOJVMhHWrhnWYznr10ReigR+qZSX25EXOG2+8yWx6xGKxYFFVNE3Ns6+8xD/9v/4zJuONeIR7xBg2N85w5fozXLt2nX/+3/0/WE4PSBGX1uu9v8f3vv89nrl8LSB+okIlEg61lLOVDIIUhUfBuRoxUE+nkW8pIrckOC5iLBq8YTQYcTybRs4dUDU0rqEcTtiYrJM9fkhVeu7Nll+Eirpi7LTqYISDtVGo3iHWfjMJrF4Z4SATDd5krXIRxtP0biXd9muVtfDhulFmx3PUxQNNunSxgVG2Ss+FjYwz6wOwBXsLx53dOTvHNXU81QKZeMXDB3f5D3/ybc6fXeeF114ns0VAZrT9ghTV7QD9gATEkwqIHYBmrbdJk/Ulq4pPOt77qq+0/9eVT30x7WkGxU8zanqf6zuORfDW4PMsoCFdeEZxLjjMSATgSfyGQWoV4FZx01auLRrFqY3Vg8J8ZuWEmy+9zjd+5Zd4/vkbrK+NyKyh9vDocMlfvf0C3/53/zu7n3wH445QlEaFjx/O+P533+LyzdcoN4ekRdTG3iWivRKikohirKOiKWFdKwLWBiWoZ9g+UWq6NXj6c5iG+rRNjDR+vRz2vqMGgcj/ZaKF0Bmu0R6KPDSSWdrgQNQPtHFPLI/krEwybUXZi5PsozJX1SElVwjXNpKxsXaWF195lTfefJ3LV68wWltDFXYOZ7z13sd89z/8Bx59+i5aHZPO6mnl+c4P3uelV37E1371AirDnrG6irtLKnbnYiDA2Pu8AKaXBtf7bvsMvV+fOm2nPo/9u4a1041oei7pDNVEyBzfiSwF4V9m0DwL6RAS5K1valgue7KlJ8fS3VTb11tUqoQgQFkUXDh/HtUmEsMbzp0/z9/8W7/K62++QV4UUcaFtPKzW1t881vfBGP5337v99h5GHjjQPF4jo6Pef/9D3n5jVcYjUbxSIwBy/6Ca4V9TI+pa0xVR6RVHBkbKua0z58sYTk5ok8M9Kk3VV3pf3u+9cZ25T3t9yvmbuIjApdwJrbf66OMui+3rta0JjUiCVSpmwbXJO4TacWtALkVNsY557cmbG2MKXJLXdfs7R1zf+eI/VlDrQbUoTpjtvM57/8oY31Ucmb0Ole2R20QJO2T8EwpHZQT/QW1go8OxdB9g0ake3s+rGyt3gXa5X9Cn3jKPPx1m7I6j0+ekU+uJ1ViEZaAaE+fCfsoZDu0CWV9ERMXp7ZrX+kb/tGFjqrQeGW5XCCJa7K7AUagNMK4sEyGOeNRQR55rI7myt7RnOlsTtNyjXrUC00Nj/eO+Iu/+EuuXjrPK1/5OiYft7Jh5SFPjEonHOM/iXrdicrB6h0kImvtfTnRdJxyEzGcObPBbDZnbX2Nrc11Hu4dMN7cYmNzg4f376LeYa2QWYMV8B68DVLFxwB/CkJI3IsSx9nXC5rlAhcLhTgCIt7HtR+eLspUDaTtG1tbXLl6jXWvZOOCyWTIeDxgNB5SjkpuvXQNq4nuInjZvBi2L1/g+eef4eD7B+yKadPnJHKzeg0Bkr29fRb1EgYDRCLnXgSJBKdgh4oLWmtH2QHJSun0BxHIM4PNhGIwYDSakK+FbBXnPNZmEQegVHXNnbuPado0zCfbz+m0EWo8mQsdtUATYZ6LRYjQD0tlvnA0XjGZZX1jDWjItAkbJJW7ilqKr5btxARhFbgPkonWSkbt60EBiJQMEEW5eH6b7Y0NDg72EWuQPMO7hi+9/gr/+L/5Z1y8dBWbBUPGANcuX+H6jZv8P//f/z1vff974b4CLWBK4a/eeovf/u3fYm1jKx5qEkhyiX4IPERPHARkjNiQHBPgHhkOC3WFebxLWZZIZlDvsdkAnMOWhkyEDBCJVZlEQgqINRTFUwg3/xpN4mO06ka7MSQiZ4KhMFvA0IIaYbrwqIaSv8PxJHCeIDjA4mO6GDSHh4h3wYFglRR9jXUlAmKKpJynJd5akkAoefv1N1/klWc2MdZT1RV3Pvghr335ZS7ffD2gWpLiH42WPC/4tV/7Fd5650Pefue7eFcFu04EjOHRoz0ePrrPzcmZKAsloFnp1o9GhEXobVS2mgZ1sdqRd5BbKtcwnc4RWcPkFqo5ppjgvLapRaAYaxHrsVlOmZ/uHAJ45xmWA9YuXmP+8R7L/c+YXB6jZogI1JXDTgryvKLxCVUD93dnPDqaMYRILBbHoHG4usIMR2EfegV12CJDjcE3Dem0NyLBuQMxeq88//JLPP/KK4DH1Q1N42hcw2A0Yjjs+BPaNEIcGOHNr3+dH373u/zpv/8jWikghKCmet57+69Y/u2/TWHLnqIsMaLZU1AidLJN1VZPvVgy398LCKDWmRiM42BQCg2G4ZUXODvdY/r4HstqjmsavFe8NRzkGfXGkMfrGQf7TZjjU20/46BV6bbJE5+Lh77tHDbpcFMfeMZs3KPRAl2Rl8HojK9DUOYRKudYTmcYX3W6A7BuPa9dyPmFL13ixsvPsXbhGpqvcTyt+fCT+/zxn7/Njz+4TRUroyhK0zTc/vxz3v3h97h65SJr25dWY1ptXk/8W7pnC84d7dBCdFF7n4gXWp6RzjRLDoP0XCfJZb+w1vJFrOpSRAW/MwRi31N0SvtupmDwa17QQje9QF0hdRWcq+lAVB8fvyc/Na2BsLe8GsgnFOdeQsZXSdX+rl8/z+/8zrd49YXrDIs8XiNEws6MSs6MbzBdfJM/2rtHvfshhhpFWTaODz99yM7D+1zeuIxKFvreOl4kajHJ6NNwz+UclvNO1RaD5EUbKez5bVplG04kvqm26+T0zfx2smhNmacslzapp+1v3zCOSiWKyXPUBgB74hppYupo+Jr0jDDt9iBx6UbEISZU0/BOsPka5eQ86mpUPeuTMd/4xlf4pb/xTS5fuUSRle1+vnxxiyuXt5lMxvzb36vZvfMuuBAs8gK7s4rvfu9HvPH1b5IPS1q0rURFs02rMVF0BDlrVPHLJcaF6LQCkmXts3ZP0B/WZDydHO3IZfZF7MuezGj/lid61r4gMZ1vZeJFIctwZYGLwUNjM6gczGeoekIFqZOprf27rKYg5VnBq6++ytmzW/imjoaKcP7cOW7ceoYsL6O/py8XYTAoeenll3jnrXc42NujrhYkBIZzDQ8fPmR/b4/RaNQi95L+3KL06OQN6vGLBXY+x7ZOEIMOSup4DvTX4+ovfmVMv8jWBXHaF070pdeSOgiosSGoE+dUVNqgRf+7K5eLf4Tl2j9TNBQtqKrAKRX3c3KmDIuMZy5t8forN7j57DOc2doiyzPqesneo4e8++P3+cHbH3N3Zx70MO+hmXP0+HPeeetH3Lh+gbObVxkX3R6MFl97trW4EqULcLZGorRi16unv3xaUvxkP9FLZf0pI36qrc/X1g5wfzPqyj37UrA9TtqPSnsEJHkVPior3ySptCRHc0e50e+Cd57lfIGRtEek1XcmmXBxo+TaxU0uXznP1rmzlJN1nFh2Dx0fffqADz/+nJ2dXaoqVnkkGd0Nt+895off+w7XnrnGxvlnUFu0nJCtqGgfoM+gRPe7EoLPyaOc1AvtjdjK30/K2NNoddNgi4yBFmyf3+JW1VDVNVsXtjmzscHewwdU3p04laO8EWVFvosECgljEGtxrqGeTqmWC5wL3EIpqTiFECDt6+gE8p5zVy/wO//g11BrkNxQFDl5npEXWeAVlbDnI2Y+7iVPVhacu3QR/5c/pKnrVm+JOzr01XumR0ccz2fo+jrGJF05PpUaNBLGBzUzrC5pqjYrQXt6KVHrsVkI3pdlyWg4YDmugh3tHM4J/YILs/mM+w9++pz8nNWjwHgoIqdSY00oz+0UXQYUTZnBvA5s63mRMRyVFFkGVR0QC+pQNWAsznl0OgtgPg9qtSUKjTlQ7XY3ko6SXsS4p7BurK3xN//G36RezhBRHty7jTZzfv03/w4XL16JUK60RYLiuH12m3/8j/8xt29/zsOHj2jw5D7mVAIPdna5d/8+482tKDwsQuDEEUIqQFDTbFR2Qp81YlRVAqLIq+JmU3R9i0MMG9qgxuB0jsg6CValxqIEhEZQBjLyrOA0T8dgyEhEMqVNEeDqkngz1DNdOAa2JMvgsHJ4POUgZ7K2xjLPoWqCw5eQ2yze4/b38fUCU4x65ktPyrSHZjQY0d7iDr8WueXF114EXgA8VVWxNRKu3LqBMUmp0VaRTCtkOCr4ha99lQ/ee4fK1RFFJHivzOY19+7d4+atl2OUv4OxJZHv1WPE4r1vo2O+bqCaRUej0oiH2ZJ8fYDig/BpltgyEFMHFFiIIIuEdJtiUFIUxakL1Kpx3N055Hsf3uF4915AcC0PsKOyXY9V7dkoheOqoXIBZukEZtFgTFFvRWjqKnI0BcGKGJx3IXIjREOjf8DSInAQiXw4GaqezOQURUyNM6aD4rYCEhLe1Nicr3zt6/z5X/wZXqvoMAt7RxQe3r3L8XTG2Y2yJd2LYjLKB20ddIF7OAg/m+eoa3DHx0FJJR34iknoL21Y4LlzLPjxVc7e3Gb06DbTaaj+4OZzmq2zHD5zhXv39yiHFpHTdtqk0ezraZ0h2CpmbZOVX01ucZaAsgJUPL6qEFd1ew3atN+WsrTP4h4PL1VBGse6WXJp4tAqEDZbD69fyPmN33yF61/9FeTMC3i7gZAxQTh7c8mZa2+w+Fe/x/tvv4u6cICph9lsyccffsDeg8+YnDmLmPLEk/Q16e53VcE1ClVF4M+JKWMmGMK6Mk7p8OzG7AswBX9mSwbDSUsgpBF2amXqsYv9bfFjSQ5mFoYj1BRg5sEpt6zQxSzwJhgDYlkZMxNgbxLPTI1npDXw+ms3OHvlcgg+R7l5fj3j2tkRo7ZKWvsUZKqcGwlffukK733vOp/vfg5ag4DzyqODGXfv3uPirSVYGxwMMSrYnstAdH0j3sPxFGZT1PuoRGTIMJQbTqDp5IQPRku/T9IRHMf9m8b5lAGo/xEDsbeuZPVb/blVBBkNcFlMq7VBbta7uwxFQ3plROzQGvxdUzrDTQhVCK9cucDf+q1f4/joCN80eOdZXyt447VnuXBhG2ttVBa7NXV2UvL1L7/IRx99xv7DOzi3R5qTRuHjz+9zsLPD9rUtVKPjt0WwRSVWumEIgYsGd3BA7mP1I40Q/qwIyLA0IT3DqT3ipXu+L9jWb1tfiia/cNtFTakbXZ970iT8sAY3HIC1IZrqAlraHx1B1QRlt71ZXMn9FLHeilIEm+VcunyZixcvBAMlyrA8s5iW3Dn1XFeus7G2xrXr13j37beo6nmYIwX1yuHhIbuP97hy+QrJmZOMHk2pmVEeKQSk+/EUmR7HMz1wI+n6mMqEsUnD0KVLpXVxYoy+oNls9TxoI+6yOiy98emZhkJwImYhRUiisW8aRZx2zsm4tpMDtl2iCRHQ12B9CGhJ01DEBa1AmWfcvHqBX/6lr/DaG2+wduYcphgAglfH+Wszti9fxxTfZv4XP+bxwTL67D2+nrFz9zM+/uQzXrp1nlFRxudIJ36HEVE6o7FtdYOtI1ZAFC8ebBwx6W02+mdjb3zbO3X602lPZbfX+l1K2tvPuFnUayTLYipUNybeeYxKi5bukJp9HF24Rx8NEea0J58axU2XZHQGOMDQKLe2S778yjVeffMVLjz7EsOz15BiDbDUlefjz+7wZ3/xfb77lz/k4b07NMs5ydZxqsyrmo8++oyHn3/E2tkLaOJtlfbxooxU6Du2tHtfvYfGByqNuEfDNWx7/p9QgWiV7FNsi2XF7Yc7XNregqVje3uLy0dTjqYz7h9Pca7pHZEBxeeaUKhGvbbr0cSKwsYYrA1UI03VUB9NqRdLKudbLloEnPhABavBjvAp2AXYQcG5K9tBJrX/dedm0iO0N99BJRK2L5xjc23C548etvstgETCUxiE2eGU46Nj9ELHZwQSPxN0Op8Qmibc3cyrEBhuPccS5IQxuGiP5EVJnmXkeYbL8rAGvNI0HhHXOoLVESsMPr393ETEiOAtNBIOhCwauFPv8Q6KEg6PmwAdE2E8HlAUeXwog/GWXEFUaVDc7gFGfUhDISg4nhSsixNOECqajMcoNFMU1otw4eJF/ou/+7fDIeWUB3c+ZO/Bh9x64QVsinoFdXZFcDxz+Qpf/drX+df/7t9gajA2XFgdVHXNZ7c/44WXXiEdDYrgkHAopKiEB4fHudhnHyJSvqkQAgmkDAvquqKaTTF6BpsZtFmE8tjGhNQaK4S89QzFI9aS2/xUEcSCYKNy4KPATkK1iTwTIsJRrWRGKI1yNK9pmoY8swxGY6ZZ1i5kjZvT4HF7R/jZDDM5SzJH0sIM1V6ICkVS5kx3OBlCJFmkVRqSorO2dQFjy0AErawIugRRN0a4cfMag+EaTXMMEvIEVYN3du/x46gkB0pLUYONqAlJ3lI6RVlRqkWAEBsJ1/faYKqa0jvUCMYavApWHXUaR2twLrCpGxHKvCQvyqcenH+d5rznR5/c4f7nH6OuwvuG6ugR5XAbkVCeb17VjCY5LD2ND3tIRFnYgkaEvJ1B0GWFm84ozmyHvhpBspwU5fbe45oasbatjNU0wTsuEnic0hgiPe0YaEMkEWrdJylWlPOXLzEYDFkeVWEOJFRgU5T58THT42POrG+G/dbC7WirBEhcwD4aQSGly5KVI5qDg5b7JJizEezvPBaYe1h4ZXm8YJHlbJx7kfUzOywf3+HoYA/KAes3v8T1yQP2dh+Sffrzi8yf3fqaqLZ7cTVVRHufW3VUSJHhy4jMkIDOY+GQxSLtzjArKZ/6Cas0/h5JYc8Nld/95mUWN9+gWe6yXCwx9YJbz17gypu/BKPncDLCa9i7opDlOc+9+Bpf/5uOT+8cs9j5DKFGCNGnR4922Htwh2vPvowpi3ZpiHSKl6GTJ2l/u2mNmc1BQslPL0Bu8TZ7inLSntjttZ9ordg43b2YLh14sCKJaTy/OpUz7T/pcuIJKQ7JAEYkOG1GQ3yWQa0hNWo+Q44Psd7hzSA4Q+ni4a0CLEllDWORiXBtPefqRkEnfMNnM1kdr/QMgZweLqwPuHxpm7vvDtBqikay+dm84dGjfZpqST6IPEUr6CHt5iY6AfXgAFPNSQS0vihwowkuEUXGzyrJ8NeYDaf9nrWy4YtqemIsnvxDe7tQnpDpAVnjMaMBzsbIvlcygcXD+6y5CrFFZ5idXIetKOiMLmuEy+c3OL+9Hu4XmS3FQFEEvUF8d61+IPvc+phbN6/zzl9ucLzca40ZVeHweM7d27fZvv5c5ygnGERtcYWoBbfGY12zuP+QM5GgXBXMcEiVZSdQM72or0h3HJyYOnnypVNq2gZlOsmfBic9TZyDNtr6ZAdVBMZjNB+ATsE5sqqm2dvHz+fIYNC/ZPdQXTeeuKaxiW0zLnxJDpXkvkzmaHsBFKHIc86fu0CRD5hxTHLAqXrm8yXHR8dB3kQjp4+WSbI/cReKc3B4hEynpFnzxmLWN6m9RkdP1/dWbLavp6HUXiGOL25fdi1Z16uOeU2Omeg89kWGL3O8BHYXxYfzcFkjw+781ni9k2CG9gE1jIWIcHZjxNdeucyz50IFL++hHI159uXXePlLX2Nt8wJE41yRQC46HHD+6oAvfbXi09u77B99HqoK4VFtqBdHfP7ppxxOX+Xs5qAFKPZbsP86RJ8Q1mw1m5M1vjNQBaTMaXpVoaR3jdWLpsBNkkPSjuMX057E0+lTBEIn2hXJDVpYXDKOYxCvmc0Qbf5PLLleeon2An2EALX1jrHUmCJY9ApkqlxbN/zqN57l9W99i43rr6PFNo3moVyfABncuPUc2do2Mx0z/w//nr17n6F+3p6BTpWd3QPuf/Yp1198jSwbRpsubZ6k6Uj6A+1oxEMfnUerGnEdc5+iYCWkxkF7BXrXPe3mnOejn3xOkecUYinKkmtXLvLhx7d5sLcfyNEl2EKj4YBhUcTUtnBW5VnGZDSgqptARGxM4HcpCwQNcnS+hKqG4aC3p0PKGdpDy6SH9HG9S8gOaY/LVpegJ/3S2ATZMJqM2TyzSZHlLOtEFZ1GP+ie09mMw+k8jG8MuqVmhDbY7eOZaxolm9UY7yP9YnKmCppbKiPYImc8GiBGMFlBUYwY5iUsa9rTJ53dKXD+U9rPbYF4A42mDnsaUUxmOPZCrUJZWObLimVVMdEhk3KIWovPMxpLgGyrIHhyFdzOY9R5jJUepDMORjL66SEz6KvnGg4SCWzbNqY2eesYr5+hmW8FhI2GbRnYpxMBZlRsrfDaq6/yh3/0+zRNNOhUqcVjVLl//35LhJsiphKrLyWHRULW9PPwmsUCnc9iqXGhqWpcmQWlCAFr8N4Hh5WGCTZG8N6RqeKNITcZo/J0U2tKC5fGyoNZcI6YRESnsdqWBn6hqQNQxhksFg3eNRjJ2T+cc9y4gDUS2sUrgD+c0uzskl+41ilLccw0lmJOGURJZPfnNU1uOp4Ei1plbes8tijDa1Eoph1qJNUVgdFwyKCcMJvlqG/CIZ4JRhRfNWFtGQs2j9WQ0g1NnNMo3KMm5uZVqEQQiaYzlNwaioElK0tUPZgh6pYYMyRVn+oiF4LJLKPB+NQRGr6p2fn8PWT2OORGesf84BHZ1jW02EDjXM6cocwtiyZmwouwzAchcqoaS9cD9ZLlzmOKS5cxkoWqa8a2SqkYISsCd493HvU+rgkirJ4gpFplNEa1+l5BiePtA7IpsxY1Ql6W2CyP85+MM+0QFZHsWowg8YDtoMQRHUTYf75xAalmLN45mvuPsNoJ5ZSuZzSUO93RjEYDCqmqGx4dOg4HG1y4uMb59ccM8oJyuMaVW1tsbl/inbd+dKrzGB86PrqsKDPdYdHJlfR3+umt4EcjFBtIelSwS4c72Eeu9DTs1nOQJF+7A9sriirjoeWFN1/G6C0gOEuM1mAKnJmgFLRRhLYngdPrlddfY3zhB8x2H4J2ysbxtGbnwQPqekYxmARESOREamteSVKyEl8ZLB8fMqgXbT8QxQ8yfJadMCyis66nyreGxtPG+gtQboy01KKk06nVQlsbXGk5aOJ51JHZhVedCDIZo8UAnQnaeJjP0YNdpF6CHSIS08ai4tJ/nOAXTQI2fCg5paFD1oRz7ATRtEZEgoFJmbG9sUZRliyqMCdeoWlgOq2CPC3Tk8YVJXpCngelzD28T97MW2i+jkY0o2EkcdUuypuUnbZDyanee77omD1tYvf2+k99LVl1suIr7Buz7ayrwKBEy2E4N7wL58/9R5jZNMxt+/1VlE7vNuG9iJAI5VPTN2jXSw/YAn2lP8pia+DaxbMMJ2tM9w2huk0Y86px3P7sDm/+YkAeKqFWnI9oUWkJyxKq1cNyQX3nHnnkgVAEs7bOMiIs2724MkIpMLQaGe4/72k3jf872Y/VG66mpKX1186GCo1Cub6JLwYIFrRBtEJ3HiPHR9jNDVyntT7xXCljUqOcknhedXLrpJzvoVmTEy6+Y4xQjkqkyCOXVwiaOjxNjNa26TBhIaKJjzYaMW3IcjHDPXjAYDYFCXVZ69ygmxPqdv/Gn6vdedpC7Y/6qbUWcdqftpXx7Fr/zPRIKB4xGtFVm1WYHeOPZ5jNYY/bZvV08PG+iZ8qfcpmlgtXrrK5+Rv4aop6F4pp5ANGG+cZTrbxbapoH0NikGzElWvPc/XKW3z48X2aRR1XnkN1waMH99g/OKS5dIY8PAxpbtGUEhxWV7KHDLDY2aVomjDPGvQ6RiMqa1rZ+LSTsJXObSc73ePUoYtt05M3/dlNQIoMHeTUmQ3cbtaAAXd4jHVBlzcS+fGijXjy8nrir6TzZEa5ulXwf/sHX0UPz+D0COdqjGs4tzXh0q0XGJy9iTOboHnvEt2qubi9xWtfeoN7D/Y5OjimOX4AEubWq3BUeT67e4fXj3ZZG2+Btb3tFNwtJwBRdKsYXOVwswUDtwyS2cT0t9ygeR7SiDTBCH76U/91m6qyf3jEx5/e4aXnb9BUDWqE89ubzKbH1NGpZCQEwXMTOGGdhiCbLTLWMstiUTFd1ogxFEXBcDSEpqGZL2jmM2pXt0ukldbJD6DanffS6f6dzF05CFfPlvZz4by0ecbGmQ1KmzOrqwDAIGYFSNhj9WLBdDkPuliSmQkFEy+bfKOCIFVFNjsGrYGAEFIf5EljMxojIW0LRWxOkVsGGyNee/U57v/lD1FrsBIDMFbIrA2Vun5K+7mdNjkRwqQGsDgBL+ngcAytYVELi6pBnTJfNCyWDTXgxAREilFQg1GoHjyiWSwpikEHy0VR71HTV4OD0mikO/dUQsJSe+iKYDRs7sFwgm5cwMZy3UG4aSQiDdVwICyAC2e3GBVjprMlXoSKCK3zsDiehkiwsYGQ2OSoOGysShNJo4NSJZDAYvVshnGOzOYYGyBjLB3WGzAB6ZFlQ9S7UNo8EmwF54YPDhVryAY5p9m2J/CNW4b/+S3i+pL2OTSZteqY1R6HpyyF6cxRNw1ic46nxyxQGmugCQqrIZAzUdUsPv0Jw1deB0LFJK/BCefVd+WkRbuNie8Mj4TeSZwVcY5dVlC5mjxOdDOfYwsbCYC1dSx4H5QnYwxebDBKnJJllrzIYoqcDSqWidB0Y1DJYrTGk2VZiOh7h04rmE7RusE5sIXFDQ1mUpDnJdIskWKNplkgxTAYPRIcbxC8yibPGAyGT8zDX7cNh0Oev3oFnR7x+OAAGofFcCZbsu89TjKMBofb1qQgq5YsYwrcPBsSKIjrNrpSN47Z/YesvQaoYmP0tm+weAiEvrFKkDUG5z1v//At9nd2yfIs5JeWBdbmDCdjbj37XEjxiE5PcQ3G2kj8HFbcsm4CKkqElbIyQDkcMRqMWhVXiKl8JDM0eNvTweF8NDK8x81nVA/vxysZbNz3liggjeGBBAhuWo+iMJ/X3F5mrI8vY/Mlt+9+zoVLVyk3NrH2dPdj13rKE2n5dwfwySh2+qv2Dru1hrcF6sK+ySulvvcA+4qjJl/h8IEuQtA/3bozThByVEIlO4+0JMD/f97+88mWJE3vxH7uHhFHn9R5tVZVt3R1idaD6RGYHehVAAyk7S5oNBq/8RONfwm5XJJLMy6N2AV2gYEY7Ijunp4W012iS9fVWmXemzqPjgj3lx/cI05kVhWWTWQh2vpW5sk4ES5f5c/7vEW7pn55YS74IMJ8u8nS0eOsXW8jbgj4gHRqHZsbz7BZH8Wyl3MBL1L0zYP/Cr4EhXaG7MEOXTugmHCJFK7dYGJixE7fPVXahfyYjtr+q4D6f31XWG0B+VUYIVCs2YIkdDrXe9xLBWp2BlufQaknHh48niBrz2DUR9U7CLpEnUro8x6OlPKpBbpKKk5IxRkTAkdCsT6mX9cGanWNMmWPkDBDuXiS4+JEv3yzSLmklAKcw21tIw8ekwSUo9MG6bZJ63WfLh2MqT3H3VTGRWTPCi3B70WJwAO8qiO3BwUTgqm6epOSyj6YrkIF2FoN2rM4EpABosAMRti1p+jWcoU7Zm9AKvTY79eqUxXGp8RQ7X1x+f69A+Kd8bm5NlFU83pR6UA866Hou4Ph1O8Wv150CKiWIVnl96pyDr09IFnb8BUjLbhYwewMYxMjVpff2e8Qq317LrilX9s2LI7/kJLa3M+PYjquAntJiIuW792Len6OQa1FSwvaCsop2NpGrW+ijhwJKSnT3ohQQu19Y8QHwKpbvUzrlXLcC3ng95irZv5TOJxGqRKNUbZU+UCPKba1mq4rCWumcFwU4jMpByPUylNqNqcsZTszw7jVxikT1lqwwzU+VT8skjLmVI7Ul3FyHPxV+AAVNTj92z557+IEmq1wGAraWfR4hN3YQZ9YrBaRrj48jLcuEUXl+CtD1JwhbnWn6ayFnlGBw3LPPp62UylFo9ll6chRmq0m/fF2+QRczqi3xc7WNll2jDg2RQ9AdBm4UuUa8fOinZCuPKNr81IhOxSq0yJV+6rclB2sjtcXfz9oJPhXvfuLV/W9/l4HuNhAo4bEDXQWoZTncJOdMXo0QHU9SmKKLA3fFwPYKfcPUJxKF7rJAN1mzOVXL6PkIsWBkRaP+NUmxqmonIvikL/a0hjF8cOHOHrkFA9v3WW3v4kiKykmcgdPn6wx6m3TXs79S4ugTxmtCYpyr9jxJLqjHLczxKQjPIG138euUcMmDZ/psn/4voZLENJJxrP1DeLYcPTQMqIU9Vad7kyHVCkyK8SJ8ZVdnaMQ98pN9aI2hiTyPlEtjqk3atTyCJWl5IMR2SRDOdBFzW/x2Ss+DTSgARXBx2JaYToMnbddXOXwrrB+pnNX0DV0W22MMuAK5H4hxcL6S3Oy4cRniBi8z4OlKAJQHH4jPptC+j2inR5uNEaMQms/bkr5ohITJeTW8vTJOq47Zm5+llYt4vKL51l58pRbG1tYdIgl6FJPf9X1GwdtjAt5Wg7AYFSOwUEqZBl0Ep+WkmaeyyV3OePxmAkgOkIbhXIFfAgmm5tMtjeIZroUs2UQPwHOooPDNz1185WKIBROUcX5eQjq4AczThrYWmeqDEUQsRhl9mw/0QqJY3SSIJEKm1pjtCLWESYKk6oL8kRByNEmwqnAzRM2O0qF00ON7fVx4sidQxuLThRRZKg3EpJG4quBxDXybIRSnpQRrXBYf9olgNZESXygkHBjFDMtQ4TFKc/0rSjkh98QudOMUiG1jnpNMxg7rHXEkXDsyBJXHxiyNEOLL/0bKz/uyjqGH33K/A9+H1WPUWivOK3DhKhlaZOHk/bC1iyI4la3htx7uM7u9i4bvT7psIfkI44v1fn+979DHDdQkoeS1ABSBn3SLGNptk6vr4qqeB6OphXtbhtjDNaCzTNcnnk3JIp86pYoxDlUQN0orcg3e+hB36+XUMllZ7aL0wqja56MV+HJiiu0mRJI4rRWmCiiXk8OHGmjleLEiROItXDrBju7u9SSGOmvQbOLNgZRQmYhcwnNGmRjb2xMkgYTZYhcTlErQjlL+uRxqTycOCRUyBIX3DOlyMaemDnSgSPHWj5+/0Pe+eVP/T4xoZ9K0+x0+D/8H/9PHDt2AlCYkpMkGJTiDdGHd24zGg09Gq5A2oSxXDp0iHan650OvJD13DX+7wo8/K9QsdaGUvJCtrFBurmBVaVd5u8K85tFhn5U82uoDEIEwe2EXi9lMonYsTm7/ZscWVrgALdiuKaObzD7v+Qete/+6VpKXU7tyAJpVCPJNIhFZ5A9XKFlJ4xNPTxy+h0lU2/R+xCyz3lUe352Mh3AQlYAU44iKcUV3eUFXNQkyqeGiUMYj/pgRyhx2CLoEIzQgg+ltKoAxoK7v0Es23g4ueBig8zM+EBUuLXqdJb/KRV6NS1iX7cO+CpQM9NWFGm8VRe2GoAoQzfTxit/ciizXbL5RdyzGCMWJjnydA21vQ6zh3zAIjilxbCJmjqoxUoXNXWivfMmIBZc5l8U1UF0OKn9onOtlaebL/WlUihlMFHNIxZ9x/c65BISYwV0bklXnhKtr6EJkPY4wc0vkMZ1ytPRioFSdQirPEXFfwJiek/A5MCuQgZUn10Yhmp/2/ZzChSOrsJFEWZ+FmsiH2+0QiPNmVy/Snz6Irl4G6Ha36pgUV/SMYEy6KBEQmzbt7VEnUnVIVMoLURRKDzgAg9f+LMWfBVIilBpWKGiQolav16KtaNszuDmPZqjkJqjNVKr4bpdrImnQWalgo00HSufRrnH6ir/+LVtyamwn/IbiCr1tFBdul/WCt+HvNHALi+QPb5PTXnnKer1cI8eYy6cI2+2KzxMYQyV77MnEg1vq/LEiASOIF9oIR2lJHFMo90seTj8vUEzBFS7tbkviFDKTZ8+lyQRtUYSUrgDUl3pYBO78l5QKCvojXXkyWMisaA0Lkqwhw4ziGMKCruiD9NUGinlTUFQXQ3sHfg19beqMZA9V2XrlRw1SikkqUGnTW4gyR2IxaQT7NNNVH7O2/lMXf3pevWfS/XhEuYSvzemaRRT+SuqwDB9xXrWhpn5BRrNJmxsV7oo5OmIcT9wVcWFNTaVAVMqgeI7DsnGuJVVIjuVzdZo6LZJ1X7pEVb4FwIz05aW/T1oecrUryjfVF0rX2hP+L8IVgP1BDot7MAQ5f5gyAwz3MZTWDofCH6DLSMFYndfNajK41Whm1RAZKFAJRRrQbQnR5/WCKNMWSnWvwoFAATodGp0lmaJO13cs8j7xsWtTtHbGjAOCFkdNYu6wJRpp8oH5FypW/zqEifIYAw7O+i8RxFUEhFsEuHqdZxWvlrYAc3TV10iYJ1lMklZXduk3mxwbHmB3uaEej3hwvwxnm1seq4WDWmaeRSQU1O7UiniJKbWqHm0oEASKcjBjSbYXp8sTcmVTyXea4tKuW6rcgw8CTvK+/V5nmPzjCzLyDKLMhELi4vBL6cE+iutqDfrRFEAB6hKyC/sdesEsT6e4MRnNYwGPertLlFUC3rEN1QrBVs71MdjtKS4rLAFNVLTZEYxmUx4eO8B128/JGvUSdptnjt5nEanzSuvXqb3/qesDYfet1EedOC5bL/8+s2CNgpSo7BKEYnDBr4WnCZ1jnEmJHXDUMNkIuBgrtOm3mySDwbBwPBGgRIPI3LDAent27ROncFpn+5knUdfOO1PxasZFtMpmwonJ44szcjyjDTL/M+TMcPtNY7V67TjOpnNGI96NJszGBNN+S8EJmnmJ6846Q+GkULRbDS9I6rEEz5qhYgBpTE6wimD5BliDFGc+OofuUO2dpDxmCzPPUGrUqSNGrRjkiQJJXUzsGMkigK3QYQSWzqvWmsacf1AgzYAtRgwgrOE1CZ/giOqQDMp8lyT55pmTbOxA9ZCog1nz5/g3vU5xtkaMkkxKCJRgRNDSG/fJ1t5QnymE4yikCLllF/ssi8tAMLnfl6vfnaX/+mf/xE270NIS2t1GjyaaXLu3EnOnr1Ivd0NKVJ+E6rg7Gxu7zJ/eI6Hz2Ks9WkyzkGtHnH46DJT9eu/J4BzFiMapQ1K+cCCtT5neXz/CcpOkEaMDFLySY4dTqhNxKe3ifNpWxNPSI3ywT1/whPIQI2m3WwceNAGBY1mk+7MDIcOHwYl9PsDtjeeYcwC0qwFbihhNM6YbSUM0wkTqxjGDUZRjWY+8ffglVHv/n1kPEa3OxQsYOJcmVPtkUiBQBxvQMSxYW5+tszltNYFY0bo7aRc/fxTjh4+ggn8OJ5su3BwFf2dHj/7yU/I8pSp0eQDNEZpLl1+gXoUl4HaItKuCCcyTFMfc3GYkMIlwO7tO+TDXnlP4SopQCnNIKmzEjVweXFq5p/nD0UcVmuGWc5kxzKqx/TSDcbj8YFO49TWr1imFUfR31M13IJjEG7OEZqH5xnXW7RGGoWFHOTJOrL1DL3Q9hXsCKaJVBzQKopB+dHx7sFeKGjVBZsGtwqDpggO+J9NrY6opOKQhZNggjMhgdxNgjNIkVUc7g3/5usjopVVjNoJf3W4RoLtzmIlLh2Lwkii0t49v6nCEPziHQd1+dbJlJ8FypSZqaPj+/dFK6vqpPkgj23GyPEjpDcb1N0YZR2sb6BWH6KPnsHWOiXCplwrOhieziMpSmcZh3IT8rTHcHeL3Y01tjfXaTQbnH7xbVQy419dPA/Agc1y0kEfl02C0eh3TpwktLqzmLIiXnDIZboyy308HpLdukV7vIvCIgpcq0W6tMwkioNenzrwXzW25YpXxVhW0kgO8irGUiouXYBxVoM2/taQvsked8N/JY5Ry0tk9Qb1bBfwee+jG7dJvruDai1RoAam695vzAJ1pIKtVOwVb5w6cms9ijD3KYO1egPKU+F9YyKCsxmQ+b8Vxj8a0RH17gy2yC8IskVUMZdS+BZoQI8mTG7dYk6GfjtpDTNd0m6XXJvSlvLjNw1WTtdo1aVVFCWdv74d6d9XnPQW41POUZBjxbx+EWng9XVuFNHpk4w++4SaUygsKhtj798j2nyZtNGqOBNU+ui1k4gKqb3eIR8Mh2xsbLC7s8P21jZr6+usP93g6OHD/I2/+x+hlNePU7nme2CznO2tLfIsEMwXkVYU7VaLudnZsu/+a0W6WiVwLILOJmTXb9Hq76KCZrRJAocPkcYRTtlST1Q1AEz1Tzm3osrUnYO+fBerMvyL1151WcywQBTjum3yeoQMxt50T4fkq88wgxQ1U6usO9m3baTcDuoL76gq5iDjCh0q+9uhpvpaaRr1Fklc+6IeFyHPM79GXKUpU+O4/MCvCSHb3vKINxf8L62hlpC3W1itqzP+hcEqpbPsndOv56raKoU+nCJjQlL7Pv/Gr/7UWkw9wczPYZ8aoqArohzGD+5hLrxBHrX8PFWWqSsmLXS06F6B7rVSvHd6jFWi8JjaRdVWq+JApnhO+HvNGNqNFvXGDOgm4gZQMs5Bai2ZTckl9wU09sIap+n/lfESwE4ssjPC7PYwdly2xyVgZ1rYuIEWM2W++ndvk3/vy3MhK1wu9LYHjFstbJojDtJ07CvQWYd1Mv2/8gdtWk0TdxWepsJojc2sR+2mE2R3l2w8wom3vqdaorCt/C8e1+ADG+lgwM3rNximKcPhiOFgRH+3x26vT68/orbQ4R/943/ITFyjqOqktKdjUHFUFuYoDijK3I+ApHH4bAIRjTgYD0eopI6Jkz3rRrkctblFPB5ODdkQILVaMVSOhw8fc+XGPXbGKXmacvXaDZrNhKNzc8wuzXH+zHHcgydMxKEjQ3umw/LiLJ9dufGl8/EbBW0EfOqO8p0zThDRZHgI0NAKrZpBYwIpL8zMzHH81Cl6126QYqenDGFCSTOGH3/C3Pf+Glr5sr7aKM+GjicSK4xhHTbXlJ/C662NjS3+53/7p6yvr9FLx/TTMUYrunHMD77/Ld5+6zvEUYJqdsH4Elx+8iEXgSzlwskjfLKzQyYp2k2hiAsLh4hM4k96beqdyxCJU8a3VYlXzgXRt7OW9OkzdJp5FvAsR0cJadvn8ZNEiOSeDNLlZbqW0mrKcK8NRkfEcXLghk0tAm0ErCoFmODrxoMfn4mD8cTRSuBhbkkzR0PB0qF5ZpaW2dzZ4lSS+MomzoZS4Q7X77Pz0YcsnT7vc+ORMmBRwIG/4KiGCRZgealDrDJyO4DgfoxTwe5m/MXPf8nS0jE63W5g6p4qvq3emH/zw1/x4ObHiKSoKJDNKs3h+S4nj51maoWqLxy+F4gtX/UJsmFKev8+OnJIo4FTCZNRn3ynT22+ifLgPZSOfNUlCSdgasq3oIKAadSbKA44aCP++e1Wh26ny3A0wLmc0WiC211BNWZRKsEoRZpZUhKSKGXiYBLF7DQ6zIx3UAQSYYFsZYV0/Rmm1fblwCvs74IO61JhlOeLwVmUNpw4dYI4rpNmowo8UnDW8eM//RNOnTnLhfMX0UxRbkrBoN/jj/77/5EbN65QEMcVqDlQNFotXnvtGx42KASlJ4jSFZSHgHi+m0gq68w6dj75xO8v8Wu7ROYpwRpY04qd3KEp6GALku6wQEOSu1OOwXjCKDVMyrScA5lC3+e93mDxz5c4EtXRKRYBuHaCO7SE3XmIdhnKCXqrz/jmdaKFU9P0r2JdFpZCYfxX3xm6LdqQK01KRD83pKOMBZ3TrU2NhMIQ83NTcJ4oPHrDrxutfBCslUSYgu+pNClV2f9SiaLQ1jC5u0Vj5zFKeUPIRZDPNJi0Z3BEpZHsKtNRwNbLrsEei6bKNvB1XOV8fsVr9vOyfeG7wdGyGuITxxg2F0l2PFE/Oz24d5vo4ou4pB0QJ87PZciZ8GSwDheQrNoYttY3uP3BL3hy/xpPN7fZ3hnSH444dXSW/3Rpnrnjr2CpTcdGgRPLYLDD2uojsvGgtLoU0Gl3OHTkMFFSK8e+XAPFCCivC/Inq+g7t4jtyBtAOkEWFrFHD/nSwoV8Kcanoo2mK1yFfytOkHzZCB7AtcdRrb5D7dk/qL2B4+JOKeR/BG5xlnx2HjfYCHvSYZ6tk92/hX5uBqtq5bOmT5g6BNZajPFpu5N0zKC/w3B3m972Nlubm+xubTPTqfPaN79Jc3YZwZTNra77re1dssm43HMEGVirNVg+dtSj6Cq9nvIFMLW2xZE+XqH26DEGXzlKTISdn2XcapEH9x+ly/fsCdxU5EWhtyrJXr/xNP27rsJl9k5R9Q9TRwyoHKJ4g6SwJacP8XdaIDl+hN78PDOrQ69rXIp6tgoPHhAvHyINBwV7GlHOhfOHO2jECbdv3ePHP/oRa2sr9HZ3SccTEOHh0hIvvfwcx0+fRpkp4gJ8KuLmzi43btxhPB77A8fwijiKOXT4EEtLS6FUPOXLi/1UUrKKoJ6u4z67Qr0oAa81Mj9PdniZPIpwTkoE81RS7Q3M6CBnJezLr2MvfvWqUJXFWm3n9ItiImRuFtdt4wZj70PkE/TaGvJsG9095PVV+RVV6pNCzvn/F67m3vcXCEGHxqkigBwITcOSno5NeI6K/B4te1e8QYiN15NqjzRR5bD6bkrZovGjpzT7Q18FswjadGeYtFrkQcfsd+Sne/FLAjrFrvlaUqT27/G9uOKigXuQqkphxWGaCdHCHCkxCd72i5xheOcBM98fktdm8NZFEZidaouiW19IlS4y2b6iq3tGR4pPwlMLoR8eqjTEkSEyMaqc22mQWHCIBJT6HjSplGuoWAtTLaCx/TFuY4d4dwvDJBCoeD4/OzdHqmolSW+1b19H+NTrihrtVpOFuS6HlhZotVpkw7Hvm2h05CtDOefT4Qv1rLSv3uufE+SRTM1rjMZZi9vZxQ6GWGeB4sCvSCEPtqEqDnL9Xhn1B/zJH/0ZW5OUUZr6w+JC7mtFfdJhbWuLmUOHyUcT8iyj0emUwIzpLPk04hLhryjLkhc6X0eG7tw8Koopcy6LER8Oqa1vYCZF6feKHZXE7Fjhyq37PNncJVWCEsX2+hY3bt3FXNAsNBqcOHGU3DnWdnuI1nTmO5w6tviVc/KbIW0EIueRMjnh7M0Fh87mjEbCTFuh8oze2PPDNNstjh4/ymd37zO0QmYtDQ+QL6skDm7dJH+2Rv3EicAA73st4tMUtBZPmBtmX4opDcLRInx6/Sqb62vBzxK01thWl7/6+Tu88PyLzMwuEOuaLytWLHKlyNKMK7du0usPfUUoZzwxnygajYQTp0+jdYSzOYjnzBHt34lYIvwE62DIOWeZDAf0nzxERZqo2SId9CHNkPGEWmpQWmO1UIvrWBvylwUw4kssa1Wm6ZhIHSzSRjw0LTZ+E9iK0iuiylpBJsIoFVoxZLkwznJmlBBHNeIkYS0SskYNl2bBiPUbRjkY/voT8h/8DvHsUom2KU6dlBgKMsnq4vdCUXP86CJHjx7h7r0tD+1VgrM54iI+/Ogamn/NH/7Nv87i3DxKQeqEta0hf/In7/Hk5icoO0JF3pkzSpMIvHL5LK3uIsFipVD8BXrD220h51959TvZHsDTJyhlUbU6E2UZ2RTTafr0NklBt0EEYyc45yuFGR2DzVA4tIpQStNsNg4cLeWcw6YptVpMs1Gj1WiQjlukaU463KKeD8njGlZpcMJwmNGp1xmmE3IMm+1FTmw/8fnzeNWXDYdsfX6F+vFTiAHtEzq9klK+gpaI9imAoYKWA86dO8ehw0e4//Au3vRwIZgCa89W+W//6/8L3/+tH3DyxCmarRaT0ZiH9x/wwfvvcvvODZCsWAGl8yNK8cIbb3Lq9OlpVrEiGEkhNRL/4WQyIY5j0H5taYHRszWGn3/uEWQUCUU+CKiVL9eZN/ucqF3j7uQkI1ogJqRfTeWLAsQWecqeu+vArmBzFQbZNGUIvrA3wmf73y7ARAu1M8eY3PyUKHxTD8ak167TePWbUJ8HCVXOmRoyKhhCufUyUUcRO9bwYBzxeMfyZKXPs4e7bK9u0Rw842+80eFbv/UyVhW59yownXhdNrSajc0x2uZUjh1oRLAw38bEcdmHatyoGAJRYEXjBoLcWac2DoTGRpEnimxplkl9FiemXCeqYriUDhv7g7LFvi/+fvDXnoBQGGcdqtAV1c1KkVcxqCXMwTQtwv/XLXWQE2ewu4/QkqEmOXL7Lmb1DtHsYaypUSaviC5XiDd4fPqCRfOwB//yvaes3LhN7oY+BVQUvdGEs7/8S77/+zPEM2cQT4Pp25tNeHj7JreufY61E/9YgSiOOHT0CEdPnfAlgov9UfGiSvRTmjH56DNaa6so560FiWvIkUPYmRnyfC8J8nQgpUSbQOHMTEOVPsl2Sqh8sJdU/mUPeqTq7AgVwzN85pAyPc0phe22UctHyFYfYlyGEoh3htgrN4hOXkBaCxTlbKd+xXQcbZ4TGUMuwoMnz/jVT3/Byr0bDPo79AYDJqMRR5dmWFho89zr3wLTopiNIlVjMkm5d/Meo93NqUMoXq4vzs9z4tTpUMlsmjrhIe1FWk5wSPtDJh9/Tre3gXI+ecDVauTLi6StjkcbSxiFCl+Wkj3LvfgYRKacT1+Do1H0c7rvhf9f1K8qTwL2OrB5o4m6fJnB2irtPANx6N4u2bVrRGfPkR86FAKmFRkjCo9WdSitA3LKO3qbGxusra4i2NJ/X9tY51/963/LX/+D3+PE6VPU6gloX4lyt9fnww8+5daNG6TphOJLOhzaPH/5Eq1WqzwMK0PX4XcVSrqbdMzw3feZ2XxWojRcFCMnTjDodEkL/VA4pcWYYNhDeFWs2YCy+fp8/S+bNPnSH6f5kgqnNW52nnxmFru6gXE+0BbtrpM9WsGcWsTFlPvEI4am/SpRcKr0FPyIao3TEROr2RpaVjbH9PojDs8knF6s04wI/BtTzszikHo4mZBmdqrJQ9eM0bRaDSKjpz5N6Mp+J1wpgUmKu/uI+nhQZgM4pXFzXcaNJFD2/y+v+X3glq/tUnvWTfWF070vexoUAhSIT4+anyGtNXBjf3ihnUKerKE2V1H1BYhiqjJkqpOqe5HKs8P/99sDyqdGSbCXdenYV+6qNLv4UVTlmRIQ3UHXR1p5TtWKnVLJkqzYLkHRKOV94t0JamObaLiDxpP3OiVkjQg7v0AqehqILYzU4jd1sNOptWam0+HQ0jzHjiywvDCHESHr90Ec27tDcmtx4gnzdUGoG0bPBV5Ma50n8DYOY7SvHhVFKK1JNzZx/T4us6iaKrMwCg65wu4v9hRK0ex0yLKcUZqSSUbBv1lw9GXjEXfv3OPskSN7fS8R0tEIF4I8FtmrzMVXLE7qtUBX4tE42sSAngah8GvEbGzSeLZBkmelTCzWRdao8TRNebi2xW6aQqyJtSFLc1YePSOOE2rnTtOabXNCjtLcHSDGUKsbIjed4f3XbxS08UaMAQeREqwEOK1yZFh6E81ZnaAFBqkFHHGkUYlhEivybh2GfX8i4ELQR2vc1jaDKx9RO34UJxrt8NFjKbeGD9YUCrgw4pQ/ZJxpt1k4ssz65tPAk6ExTsjTjPuPV/nXP/wz/pO/8Xep15vTiB+eHOjug4f89Kc/Y/3JYwSFwaCMYBUcOXGYE8dPhs0YyCW9VVw6goppyW60oJ0i3dgmfbqK0QqpxYg0yCYOu9snmgflohAlByHHKMEnSBmfGiYSyir7ST5Qh18pakYRqVBBBxUCFoWz7A2vXGCYOWZaHjU1HGeIs6xv9Pj82m0Yj9g6tkynPyRyftOo4L1l958w/vwq8bcXABOI7KQyn2FjlgKvuIR2u87b33yDJysPySY9nOREOvCY2Ix3332f+w9XeOWV16g1O9x/ss6dG3fpPXuAy3tERoNWWCXEojgy2+Ttb30bE9VC0Kgoi4kvVV7hh0AFMmMHgzur6N0Nv14kpXnqEOO7OcnyLKaTgLMURFRWLKqEeRYK0/cvigztdnMqcA5sGj1/UppO/PxRsI7H5NmIfHsVWZqhOPucZJZ2s0asU6yFzc4cYx3TyMcoLXg3W7H+/q+Z/973iRstJFKhlDfeaVHKkzk7B9rgcm/4Nbttfuf3f49/8v/+7xiN+qVhDh519OzpE/75//hPiHWEUgbnLNZmOGdL4V7sZxf2+qETp/jDv/V3SKK4NAq1SHDuJQRzNeKcP5WOIiRLPQeWCJu//jX51pp/shT5qyrsY8ibMUefy/jtaIWTz57x2cZxVu0JUtX0Mo5gcAhBoRcOx4FOYwnTp/rsfS+aQq39H/cnFuTGkJw4zKg1Q2O773nGMkfy8Cn27i3i598gJyqhqoUB4kM1BuUsBs9B9mx9xH//zz7k/kd3iUZraNlCyQ7NZMKN2hlefGmZ5vxRqueI4qUYd7ZyHt/fgrznnUQBLY6llubI0Xl0rYXzDGglOseno3qPxjsMQv6wh3n4gMhueEfQaVzdkC/PMUkaIbVVgr0i5Rj55khp/IJMuVrKa78beQCXlDZX8SsQSPmq+4d90k6CzGX6t8KMzRJF7cULjG5dIRqP0NYhq5vYzz+AUxegdbREnJXpUMFR9Lyn/mlLC4vMHLvInZvXwQ7DmCk2+o4f//QKCY6Xv/VtGvMncSohG4+5d/sef/ov/5jN9WdhfRqUimi2Frh8+SUW5+bDwtWEHevfp8Rzzrmc8e07qGvXiNJx2LMa150jO32C3UIV7BmPyuBJ9cciCFGEHQQTCAe/7mu/M1ruOil+C60ugjvhFxFwzTr58WOkN7vU0iEKhxlnuFv3cA/voS92cSZB7V8V4Vk6inEhlSB1hrtPdrh98wFiBxSInofPNvmrn/2Cufk5ls88D7peup02t9y7c49rH3/CuL8V1p8PedWTGi+8+Bwz8wthDVaqdgkUVcUEUM4xufcIc+0aSbqLr2QSId0O9tBh71B515HqvvIpc64yNntlVvjo63MYg0ypnmFLZXFVSYj33CN7myRociU0L12k9+mnNJ49JhJBpxPM/Xu4G9eI5mdJk7o32AvZHfaHCkREIgJaOHL8MGcunOPZsxXyzJbvyWzOlStXGI9HvPraqxw9foQ4ThgMRty6eZcPP/iA7Y01VMlRo4iihPPnz3Lx0oVSDkgIPPmDDSnTiY2z5DfvYD78kIZNAY8UsTOzjI8eYRQlXwyiTRc7Vem0x4MtnfKDncg9yIs9erHyX6n+OBUoDo1tdciXF8nvPyAZpSAOPdxGP3gEz59BL3emhPzV1odnOgJNg3ievt3hhNsrPW492WH12TY76xsMdnYwxvDyqxeZfes5arO1QrvtWWtZbnm88oydXr/sG3iuxcXZLsvzs75aTJVnDkquuMJEUE7Injyl/uQxcTZGqXDgm9Rxh5bIWo0y4PNl4nFK/U/pDO/RjF+HUJVpoOJLn75fKVZaZBODnWnC3CxuZQ0tOUoU8W5KevsWav4UqtUIaMHiwGDqL5YjWchrVdgJXk9arbACo0zYHWT0BhMGvRENmXBsscnCQmfKH0cxb4WPr8gdjHMhzZ1Pbwt3qsBx1K7H1Gr1cPg5DQoUAcIvLDpRkDpY72M2NokmPq0YAA15u0gPD0VwitEqhenBC1SjNctLCxw9ssSh5VlatRg7mRBpYdAfsrK5g0UwkabVaNBt1tEKbO4BF0X12PE4ZTROQSl/wGoiZrsxOjHku1vIxjNsOkHTwaiA4hOwWYoTi67VKXSSRUiaDY4dO8L6nbulHVUOBUI2nnDj2g3eeOVF5ptt4lpCZDST0YD1Z2tMshwXNoCTEFwL09Botei02kSoyh6s4IDF6zqdpsQrazS2djGuCGiEe0zEqFbjSb/P1nBImluUWI8yFUXeH/Pw4Roz3RkuHD9Mu9MmbjRJ6hG4jPGg/5Vz8pulRwk+bcl4ehe/TiyiLeIck0yINESxYji2uFAO7Oade2z1d9mdmSFv1rG9IYnyJ1PiFDaz7PziHWa//T1MdxZxnuC2qHbiifck1C73JpzWfpK0QKMW8a3XX+Xu9auQObA+8GCVz5v7yc/+CjfO+J0f/B4L897g3O33uXP/Pj/84V+wtbKK1opIhypCCO3I8PrLL9HpdP3+Khwe7YM3FNwaqihF7AMTYoXxg8eo/hCjDLbRwqJI7Zh4rotumVB5Cr/87BiipiczUworButAR4LSoA+YiFgBtUhTi3Sp3IWKMxyCL1aEYSYcTiJygdHEk8bWGnVyK7jM8XCtz2GlSZQmKopfagWjIbs//TntV15FOjM+SKd8Kp0Nc1tmZeBP6YsUGK0U33j9Re7efci7v/o5zllyJ7g081C63LLy8B5PHz9C64jM5jjJfW6htl7IhuBMMzb8/u9+l+XjF6FSFcGjmFRoVzDGZaqk7SRneO0ebjLEl6wyqF6O1OrUjh/GGYdYvwcQhzLxNMVFa5yFgiNJa0OzUffkuAd4aa2ITQz1Oo1mE20itNJEUYQxhry/QTw3wkaeKFFEGKSObjMmHVgGtQ47zS713tg7ROIDdoOH9+nfvcPs5RcQsVgMkdblGCnwhM6ll+pdqVdff4X+zg7/9o//Db3+TiDNDBa7EsRZ0pAzqcrvTtdkWTVJaeYPH+F/9V/+Y04cPoqq+L1Fbqv/kvKlx0ObtPHBU1GQ9nps/vKXJeLJBAJshwexRkaz21V0F/scMsJca8yx2dtcWV3neu88W2oJkbh0wpwCdbC6sHJ9mbLdH2SoOLlK9tzh3SUhazdJzpzDfrJGhPUpe+u75FeuYk5eQlqzWHzQyhTzHfagisBXzYC5Ohxp7rCSf0Run2ElQythkMHHN+9z6N2PePt7XZpNX8beAimKxyPNT957SP/RLbRslY5l08DZ4zMsHD+BxB0/h2F/FnaKVNaD9Cxcf0p9/TZaer6T2qEWO7jDJ3CqVVYHV+EBhaIuDSEfwfjiGJY/HbxxqgrctXKVHG6mFuseo6KY72mLdOUTUb4KYXx8Ac5eJLu2Rc31MWMh/+Qm0aVfwwszELWxUgQPip65MlCLwGwz4pWXL3Hv+nW2HvRBBoh4DvX7mxn//IefceXmfY4fW0LrhNVnu3x6/TFPe7lHNCmNUhH15gzPvfIir771Mq6s6hhOdUMwXjsFWNzmBtl779FcfYwW66ellpCdPMZo+TDjfBomKHxcVRrcYbTC7wooSl+XQduvydOfzk1omBRBv+kIh4/L5VUg5abcM/5zZxT6xCHSQ0exvXWUm6CsQ60+w33+OdHh47jZ5RKhMV2hflPqUCLWKFiY63L4xAnu3b5GNhgh4g35YZrz/ic3sfm/5NvfXeXUpcskjQ6jUcqdOw/5+c/e486tK1g3wstdrx+OHj7Ea6++hI7igDwOhQRCv4txVyKonV3sh58GxFQgro1j8kNLTBbmfZpbkRoM1egVKpDhFv5Ema4jVR1wsHO5Z3+riptacewKR843YZqqUIiNqcvtv+sUpJ0W0UsvMvrpBq1s5NNtd7Zwn36CPnYUc+Ysdo+OF6Ypv8XMOrozbV559WUe3LvL44f3cS4Qp+JIszE3b13n8ePHtNot4jhmOBjT290ly8ahVR6BHUUJZ8+c5nd+9/vMdLuVAGPFvQjySItDnq2T/finLJYBPI1N6uTHjzNYXCCT4lBy6gh6X7fidRfjVmyCsl8HPYuVIaz84uerYjcoKv2u6EQFNkmIjh5m3GlRG2UYceh0iF55hDxYRc+2sDVdQThOv+/7IyhxoZKLY7yzyY333uG9j2+w3euRT0Zgc5J6k5u1jCOH52k2jtGpa48+DQ/LxfFwZZ0btx/R640Cism/IdKas2dOMDfbBRRuX3/39AkFkxR78zaN9aeofAI4nDLkM10mh5Z9VcXS/9fsHcAijas6nurrUIV7rhDmoJDae62+/T8VgdQgJzTYVp340DL56j0iSUEgSRXjqzeoX3wF3ejiVJ1qcYZyj1ee76z3p5T2Qb1hDo83B2xu9tneGbC51WNra4fe5gZLyZhvvnKK2dYlTLNLQRFQPjNwuPVTy05/wmg0RqTgDfNzHytYnG3SaLXRei8aqNhgoqZz4rlewfUyWNnEbKwR5X18gqZDtOCWuuSNmRDM/6J983VcURz5gM2hedrNBGVzMpezs7XL5k6f0ST15diVIs9zJllGrAoeWqFg83XWkuY5oLDOsdvvsx0pZrWgbc7w2nXsW2+SLyyEas++TzbLGfR3aC8sEMf1PUTrL77+Mp/duY8nXXcl8gwByYX7N+7x6w8+4ttvvU47ruGynGcPHnH37kMGmU/z9VysKkyd1xHduQXmZmeJRZVZMF6HFR6EFzzRbo/agyfUhgMIdk4xF3mUsJPUWd3YZCzFgb63paJanVq9ztz8IjPtDpHWWOWIjcPXcdlfjW7fnPxGM6i84xOJNwy1E4xW5KKJRDOegI4MEsNgYn3H4hrZMCXPLE8GA87ENTo6I3c5EUKiNFZgfPsuo6uf037zW553powkunKiNLpEMJT8KKGa4psvv8Inv/6Mzz/9HOUyxIgvhysGm1n+4ue/5ObNm5w6eQLn4PHqGqvPVpkMfGlao71r55QPPFw8e5pvfOMtoiQpofv4eIAn71MBDC0esQIg4sjTCcNbN1CZF6pxBOrwIXYfPiY+dgTd6KHwExwTYUPKmFOaSEUo5asaaa0xRhPFBxu0AYgNxMajhpy25NaRGL8UdFjAWmA0cTRijSjDcOIRKt1OiyRp4GzC5mhIWmrsQGOqBGUM6dVbDD/9mObb3wIdDA0TlYadtRYJFaCKTSp4pdxq1fg7f+sH5JMhn3z6EQ7rnQf86URuc490UZlHN4nzqCYxIdUDOknM7/3WN3jlm98HXfOLN5QuLsgWtfaOPjrwJQUHeLy+S3rlqof11+u45y4wvLHCeDJm/sRxRuubiMtQ2lC4aGW+ptLk4YRPoVBaU2/VMdFvXKjt330pRS3xzPeNRpNWu8VoNGQ8GfugSpqSjTZR3Tbgo8nDScpct0FNTxgTszJ7mMXBOlHhnIggoxGbv/wl3fPn0bUGGOfzZ5UqyZuV+D2iTRSizoo41nz/t3+bI8dP8Mf/5t9w+/Z1bO4rjElhLCufolNGYSrKVaFQUcS5517gP/77/4BzZ8/hDRkv8KopJlJ8NyBnVOSVRmEPDa7dZHL7JoXZrdXUBNcoXByTnXTEzRQtmsQIp2qK2e4OJ9Y+5bO1Q9wZn2aoZ1BOBWJwKsbrQU5jVQGHQPUe6yYYPJU4V2E/ePnjv2ubNey5k4xuXyHuT9Bi0eMJ5tY9uHcb/dzLvspLxYCS6hvCnmg1Ii5cPM71j1rsjgoyTUfu4NnGNj/8i79iJ6/z3Euv0pyZIVWalb7lg89X+PRH78DOdRQjABIlHO0YLlw8SnPxBEitJNesmqRe0TrINe5hj/jGDZLhY5AMpYWsLkxOLTCaOUTupjnsFA5h9RLFHvRHpZ/Fm78eE2cKZ50699MJm/L4BGOm+L2i6PcHLrJ6RO3yBdKVhyRbd0EyzNoI+5e/wMwfRo69hFONL/RISqSEItbwwvlD3P32t3h3NGC0fgNlR4AnDnzaEzau7BBd3UI558888LLUo0hjas0ZLrz4En/wh3+N2a4nXpUwj2VwCkAcajwk/fxz4mvXSfKJl+vakM0tkF86z67RBNor7xwXHmLVIC4H0jsWqpxNNQ1alt872Gvq+1ZSTKi6HZTw7WmwseL4SdEzh8y2yS+cZ/L4PtFgHRDMaIRcvwZnTmJebONqHf/kYk0oypPcokXddp3nL5/j9o0brNzpoXKPthGB3jjnnY9v8vDJU44ee49avcVgnPHk6RarzzYZT8aAdz4jHbO8uMx3vvs2R44fQ4ega/VEf5p87lCTlOzz6ySfX6U+Gfm5MQbXniU/cYJxq0MuAD7lTgJh9Z59Vzja4RdXiXZVlv7XdJXut3/lntW6J7T6Jd+a/oxAqiPMhfMM794huX+bmrXoPCN68ID8gw+Iul1YXAwpb2D37OVwwCcQoblw9izf+853+cu/zFhdfeKroBROinX0+j16vd4+tz2sPa2pxXXOnDnFH/7h73H61KmpPpRp4Mnb6iHQubNL+vNf0bl/l0hyL6l0RL6wwPj0SYb1uk8TKEdDygdW97m3nSrBdqopYQc7kdM1Q/CRvqgHy36Htk53osJpg11cZnJkiXxrB5PiD3E2n6Jv3cMcP0J+qB1seoGysunUVlC6SMkQGgnMJEPM4CGuP6A4ALSjCSt3bvJ+awZRERfOHGK2GWM0pFnOyrMdfvXOp9y9cx+bT8oWaqWYm+lw/sIZmu1meQhWBA1RXoYUBPDKCXZ1DXPrLklvF2UtgsbGEenyEuOFeSaoytqV0jb44vjsMzGKaz+08ACu6VwFZ7pAwlZfu8cSKT4RnFa4Zh11eJnJlSb1dAjKYaxC7q2gHj9AzyyjWgkSDp2UhLTLiompUIjLg33usVBbw4yffnCfq+9fYbS9Qj7qYdMBLuux2XIs1TY5crjFoZMXERVTbq7QvgzNyvaIldUthrs74CYQ+GtQioZxHDs6T2NmBkxRHKNiA0B1Afu2WXDPBvBoBbP9DCPeN0WEPAF95gTjqA1ZUaG1iFpOs0+UHOxOjI1haXGWViPBaLC5Y31tk9uPnrI7TksbNMstg1HquU9rCfVajAayNKc3GpPmOdZ5PSQCk8mEzW1hwRjmnWVy+z757Vvo48dBBxtPQa3ZwO4LiBWS/MLzFzi6sMD99TUynB/A0oYWhjs9fvwnP+HJnQccX14mG6Q8vP2IuytPScVSFB1C+TQulEbHNZaPHmZxds5XKBYB5bBZTr/fp9WdIYlidG4xj1epr6wS5aE4SbldDXmrxXB+hlo+ot1u0XcO3ajRmWkxMzPD3OwMJ48tc3S+6yszjobs9nqoOMHEMbH56kP+39iTVNY3yiNMfDsj5yFLw9R/1owN44kn5YpjQ7PZJIoNk9GEHUmZEV/8LAlLTTkhn6Ss/fhHNC6/hG618FkuFuWhLX5MRHxakS5MA1VWgWq1Wvytv/03WdtY58mTx5hQOYgsR1ufvvVk5RkrT9ew4rChjHFkvCsn2osxI8KRpUV+8Ds/YGnxcOlUeeK6KVQOwGiDQ2ED7Yc4x2hzk/Gd2740Y7OB5BBZh2o1aRw/ge3dwxM4ReQOnI5Lw0kXKWDaU7bGKiKOo4MN2oh3YmPjcMUJplYh+EBALnh1MZpAbDQmMgwnFqWg3apRryVYGuzm22zUY+ZUAmkKWpHXIkanT9N9ssH2H/8pzUsXYXYep6vOildOhUNecMkU78fBzGybv/8P/jbHji7yznu/ZjDsM84tORYdlGk5ZqLQRqGVIdbCkaUZfvCDt3n1ze8Q17og4XQvRIhKFAghtQaFMiE67ITelbuojUcomxNFDQadFoNagszMkRw6yXhnhOQTylPtIhgFBSrbi1Htgza1Wg1jDjZoo7UmiT2kudVs0u3OsNvrUU8zJlnGOM1wO0+J20dRxF6455ZRLjQSzWTsWJtZpvcsIZ5M/DzgyWQ3P/2Uw0+f0j5xynMsFejOirOipFj/IY1RQGJ4/qUXOH3mNJ988gnv/OpXPLp3l+FwEEjKfBBAhfco5XNFG60Wh0+c4K233uaNN96gOzMHaqqERcBpXQrsIufcI8IccVIriS/zyYinf/qnMPapIDasj1h5YWe0YtSJaJ/waWE6tKOmFQtG0axZDs885NbTNT7ZPMNTTpKpmkf8fC0eRmH2yh4l7j+Ryufhs0rgqNoaaxT58SU4dZL02jaJdehcUKtruM8+wywfxS0eLiGhU8OumFdvKNUiuHzuMLefO8dHOytMxrtlrCq3OSuPH/Pn//Of8eGVRzQXT5KpOhvrPXbu38dt3CCSZ4Av3bjUULx6YZ7Tly9iWsuhilUl27xwjJXyvAtbDj59Qu3JLYzbQpRFlCNdqpGePctEz0I+nf8yeFN4KaVFqoLRWzy/4kD+e83Vl18ecUkxoqXjVKY9lR9SnkBNHVZVruuSIwJBsDityI8uYM6eJ/9sEz3e8BVNbz7D/uxH6N+poRcvIbpOWY62sOOYPmu+pfn+25ewTvjoFw36K9dRdhckRwRSq0hDqpMSh1IGdIwydVozi5y//Dy/+4O3OX5kiYKPrHDipmtTEDshv30L3n2P+tY6PvPeYOtd8guX6C8vk5YB7Uqg4wsDWjXk2af/Chn0FZw4B3JNcU9TN2LaNv/faqhs313BKs+NpnbhFMNbZ0lu9khk4gmJVzewH36EXj6COnYO0cn0u2HdqJBegwj1SPH8ueOsvv02f7k7YPfZHZT4wKgITKxw7+kO95/ueJ468eS1xemzUgZjIhYWlvjOd97m9TdeI2k0KMqWV/eIXzoO5Rz23kPkl+/S3H6KEl9e1cUx+bGjjI+dYKwTqtwvxSYr5IWlwj1W7IaKt/iljuMBXlNZuTeIVI0zePE3dWfL8unTb5Q/pZ026sUXGW5vEm1uEOEwoz5y9XPU/Dy88SZZt1MJ8AdnOdgeCh0C401ee+01QPjlL/+Kx48ekqZZ2ahySCtOt1IaYwwz3RnOnz/Ld7/zNhcunvd/q6ZKlD+F3d/vk33wMfWPP6aRDkt5bxt10lMnGR46xCRwwe0dm+k4qMrgTbei75+e3nGwl1Rc+VIv7nf2/ed7pUO4tME2u5jTZxjdfUSSDRHJ0aMduHcXuXOKePYcrlHUlSzQZaWFSpV4ttlqcerkUY4st9nu75I6/7m4lPHOKrc/eZ9er8edCxdZXFog1pre7i4P79/hwbWP2d16WqIxlFI0azWeu3Ca0yePkyQFu3/Rkn2hMAX0R8j129SfPCbKJ0GCavJWk/z0MYb1+pQE9gtjNA26laO236f4GgI21VbsCVSU4lJVJiy8vwQIhUPUWkK+OI+eX8Q93SIK9mOzn5FfvYE+dJKo4SvYFYE3xbQYCEphxNvLhb2jEcgzhpvPeHL1HSL3FFwGAf+5nWuuXr/F/OI8rZklmt1FX3UoNDZHsZUqrt7Z4OHdR4y319AypqC21jjmW5oTZ48RdxdwAWlTbqk9AaXpoMjY4e6tE688IR6tA2NPV6Fh3DKY0xeYZHEpt0vuqYp9c9CXNopOq06iFc7mbG3tcPXGA55tD316duDlKnlZFV7oa293W+cYT9Iiyav0Aa11jCYpA63JrGDyCYOf/4r4hRfQh4+G8ugKMZqoXgdjvJ8cDmpR0Oy2eO2Nl1n/0c/oZQHcUTgG3tBid3OHj359heu12+RZTpbnWGdLLk+CXEZAtKY1v8CZc2fpNpt+1QbjTUcGEyeBb9YR9XvU79ynvrODlqot4IONo5kmO62YueNLPL91Gnf/EarZYH5hhk67xcLCHEcW56hpob875Na1e9x6+BRrDN2lGV556dJXzslvxmmDd3oQyLTy0Gc01nohNyoq0JmEwdhD9GOtaTdbNJI6uR2yKwqihMj5ctGiNFmrhum2GV+9zeDjj2l/61s4m3ueFWdQyvhJdIKPPAZ0RHFaI6CU5vSJ4/yv/+E/5J/80T/n4aMHaGUwsSMvBDICGJRW1NBkOOJC+IlFKceJo8f47d//a1y4/CLKGER51IkLyq4w/JUu0APaB5ZQSJ6xe+UG+coqylrUzCxy+hTbn1+DTpfmkSOM0lV842s+UORlDUZkr6moNKI02hws0kbwkOtG5H/z5XiL6g+FIPHvG6Z+zOpGMRj7U45WvU6j1iB1CbbR4P445YhSJEqRWw/XxCkkzUlv3Wf7h3/B/N/5G6ioVSKmlJsa/n5/hTkNjpavLKZod1r8/h/8Di+9fJmPP/yIz698zvr6BsPhCHEOrUFbn7bViBOOHp7npRcu8vI3XmXu0ClExb4gS7GZoQww+O5qVMHw7ryRMN4dMXj/IyI1RmKNq9fZ2d5hmI9YPH+WuDWLrjURt+OFpvP8KmjnqykhWPGQ6qIiWL1WI4rjA5tDgHQyoVZLPJosjmm1ujQaLbI0p5ampFnOcLwLk22kvoDguV762YTZRp0odYzqHdY6y7SzR8RScB4I+fY2T3/4Ixr/6B+hanUECQi7qSHqnE9BNMqjlaxzaOdQ1tFsNvnmt7/NW2+9zcb6Gk9Xn7K+vsluf4fxeEQ+SYlrNeYX5lk+dJhDh48yvzhPPfHV49A62DAe0irioaOFxRgyI3HiuRtMYiCUOt5699eMP/0YjSexFvFVdVwI3kiikbMZM50xOpyLigJj/CmANkIcK5rNCSdmr3N9dYXPe+fY4hBWxeVJ/8Fd+51X2RMA8Ab7F+8u13PIPRARbLeFeu4Sw5XHRFurGCymP0au3IAjJ4he75A324UrUQYVCoNNB+F2eKHDb333dUY7K1y7+gmT1JYvtzajv7lKb3sLUR+giDwizQ1Q+FP9CFhK4M1zXb7xnZfonngRMTMUUYtKDGNqxIzBXd0g/vwq8fAB4JGKkyQnf+E8w4WzZHkSDAOpjsCXugx7eGykat5/fcZp9clS4cyo3jB1/kPoopRF1ZtDS5Uia9dQl84RrT3DPBph7Ag9ctgP7yLNvyT6bo1s7ixO1adOqCsf6tc2cHI+4a9/93nm5mb49fsneHrnGvnOCsqOAUsBUxNtIGoRd+dZOnaUF58/yxsvn+XMkYVQH2OqK3TVUrQ59sFDsp/9gtb9B0TWQ6FdVMeeOM34uUv0o31OfnhOcTJW/F4Ol6PkAvNGarFDmJLIH/g1TTSTYIlWOU/K90MFhr+vMmAZlBOyboPkzVfYffKYuf4qxgl6nOOu38UtvYvpdnAzh5FgQxRL1B8UeQJnBcy26nz77ZcZW8Uvf2zoP7sL4hFTnsuEMjVi2hNQRMSmzsLSIt/69jf45nfeojM7V/ZlqhepfFlwa+tkP/sVzXu3MG4U1pIhn5knvXCOYafrK2+W7S3euNdNlMq/VIIoLiAa9Ne2Hat7vfpfVUEyhTYKFGHE/dJYEdIYABdpspPH0RcvMfnoI8x4gHY50dYG+QcfIM025uUXyJpNpug6VwbgRBVBNOh027z+jdeZme1w5bPPuXXrNs+erZOHFAJ/o9cF9bjG3Owcx48f5fz5M1y8dJ5Dy8sYXexIVRnikETlBDUaYT+5QvLOezR3NlDiEYguikkPH2Z47jT9RpO8shYKx6s6YoXLXQ2bVNmCKkxpB3btf5ra98P+efqyy8U13NETTI4fIbt6ixoOZVP05mO4egWOzJOfXqSovqeY8r6UB29BT+panaOnTvPKixfZ2tjg8dbIF3kQi7MjRjuPeHRtl7VHt4kbbRSKbDxkMtggG+0gua/gppSiHkVcPHWUN157kaWlJYwxZZpl2ccKpEjlOe7eI8xnV6n3tqYk0kaTHj3M5NgxJiUNenXQpt58ueRLM1hKv+LrqRr1FdeXinpVkQ2lVQIINlK4+Tb65Akmaw+IxIJzRBMhu3kPztxFzS0inUWKNMfpQPgfXBFIQPkgDsJcI+L580d4vxEhvQyk4HmCca6483CV7OfvMcoV3/j292jPzmNUROYUO1bxy+vr/Oonn/DsxucweopSmdeJCA0jnD8+x5EzZyDulLb3tM9Ffwsf0h9a2acD3PU7RFsPMfQg4N+ySLBnlhm0lrHDqNK3qnRVX24I/XtezgqB+p/d7T6ff3qb24+eMXG+MIkOlUKVUj4zJIo8ITGq9H8Aj+Ivui7eLrdOGIswsQ7RmuThCvlPf0n3b/4Bo2Z7GuhREQblD9crUxuZmMsvXebenYd8dusOk4KuogjchJGxNiNNBZvZUEBoOg9FKQDRClNrcfLCec6fO0ktNnvoupTStNot77dmGfHdR7TvPaKepR484A0ZREFWr7HdavA0HeCM4uLz55BazE6aUWvUabUaLM62SCRHJinrq8+4/WCF9dGEertGw04Y9HtfOSe/cfUop3JEK6zSmOLUzGhwjmEO1lladaGfC7lY4iii3Wz6AarXWRuOGdVjOjbD5g6HJlucJW3WiXd7rP35n1N/4TJxt41T4rkXnAsD4h0wnCBKk3jLCBHtUThaceHiWf73/9V/xZ//5Cd88tln9Hc2cDn+6AlAOSxCJJ5MWWtFlMTMN9pcuniJb37vu5w+fZY4jlGh1rwoH1MQ8flvIoK1OUX6C8oHItLegK2PPySfjNHaYNoN0madURQTnTpOPDdL70lEZjN05AtlFoEnz4njDTAXHE6jfQWkgwzajDLhz6/lrO568iUnoX5UWXLX4MJY9cY+itqMoT/yhmGtViNpNBiPoTE7w+OVNZ5poR2cEDceU79/3zOBO6H3k5/RvHye9kuve6HqBHQRHgjEeaJwTnmeolKR+AWntebY8WMcPrLMt7/1Ojvbmwz6Q8bDEWmWUosjGvUG3dkZZhcWqLU7aBUB2hvC4iqKKnCgBIieQzwPCiGhQhQb79xEbt9AyD3B9tY2vZow7EScfv4iEKGiBi63aMnxga8MJx6iWQiCgmBVK0OtHhPFB420MTx+9JC55UWiKKLRaNBpd8nTjHSSMp5MiLIMu7uKqc0CglOabOLImxDHkE4iVhfOcGjnKS3nTTfPG+HYeu99Ft9+i+4LLwby5gBZLQJrlZQPFxStDmgiwaG1QUeK5aVDHDl6zAfkytSBECRQINqUEGQfuAv3iPOBr6qjJBKi6T6QYdNJcCFjzwOytsGzP/ojbDpAKQlIMr+uRHwEfLIQ0bqQYnRwVJVDS5FD7cKeBhN5RFK3u83R9Y/45Nkh7gzPMzrgFKkv2kvTD0oVJdNAjrfn1NQgq+RMWaMwZ44xeXSR9MNtonSIsr7UsH33fczcInLpeVwUFSPq5RseOaW1P3GItOLC+VOov/F7JHHOpx9+TGoD7kcEyMCmKPoliqQgyEy0cDixfPNihzd/8DJLz72Nqh0FScK+KJR3JUyRaeydIfqda9SeXUHLNihLbnLSs01Gl15myNw0JWa/0VcNHkBAR4Y79gRFZGrHfg1XYSYUqSt7XfzqjXtdH8Ht8UpU0XQnOAPZkTlGF5/D7G5T334AkmJ2cuT9q1BPMG9FyMwpHAlFKqIqx8q3KlbCsW7E737jJJdOH+bqvZe5c/cx60+fkg6HiBMibejMdDh0bJkTJxY5dWSGY3N1ZmuGWCmUC9UgVNEHn1DqXI6sPCL94Q9pXb1OPPEVp5yOSBeWyV65TG9uhlyCwVZBRpVcRKoI1PpR8O+ofFDM6TRO8DVcfhKmKTxUbGM1PWAIYzx1fPYkwux5otPgTh1GXn+d4S9+Stvuopxgdgbk732AnusQv/F9ssas1x/l+yQ838N4DYrlmQa/+/2XmZtp86tfvM+jm1dwk22UpAg2vDukEiuD0nXqrQ5nzpzkW2+/wguXLzA7OxM4+bycLttbHJWKIL0+47/4Ba3PPyVJfSqIKINttkkvnGN06gTDgKOqnh6LVEeg0IbhEk0VQ7BnaA/wKoJXPk+geIPaewNqD2JxbyBAld+ZSpFCzymyVhP13EWG65vEd2+S2AyVZ5iVx7hf/RKdxJjLl5F6gwJZVMiE8sAorJtOp8WLL7zA6VMneLr6jCdPVlhbW2c0HOKsQxtDp9tmeXGR5cVFFhfnPWFlrRZ4EQvL0W+KcD7l5Ww6Ifv0c5Jf/BXNZytEgTtHlCbtzDB+/hL95SXS/QJxDzlvcHQrG8/zS+npZ5U7v75rusdUcE6rgKjSIS4DcV7uCYa8PYu6fJHe4/skOzlKMvR4F3XvJnx4iGSmy2Q+CTJ4GrD1v1bHV9OeXeCl119nPNjm57/6mGd9i1UaEc/p6UYbZOOdcnzEOQg2opcfilpkOHtsmW9+8zVOnz9PUm/sMwKmstHvf0Gt7+Le+4jWykOifFLaX8NGDXv5Er1Gy7s3X1jqU9lJMYL7hOd/mIBNBT1GIf+r4n0asPG/VQI4RpF3GsjJ46hbi9S3HxKhEAfRZh/72RXU7ALJc23SuFVJUp4+cg8XYtiNtRjOHV/gwuXnuP7+KiovSnH4w8BRmnH7/mNWN3/Iu5/d5cKLb9JePEovi7l6c5V7n1wj3biDyp6i1YACEBAbONZVvPbaeTpHzmOVRzRqfGyweD7g0SiAEoUeGybv3af58B7JeAOtUu/vamHcMZg3Xmd7nHiFUp1AxZ7fD1qmigh5bnEodrdH3HuwSn889hw8SoXKeAqtfcAmigzGmBC48fa7Fk9KXIiSokCD1YpMK5wF5yzG5sSfXsWcOEb9zdcZR3VKPrvwf0+N4p+jFSwdXuDtb7/BTn/Ew9VVcpdSeCSuQJCLBD4dP0J+VxdBPEAbVNzkyIULfPs7r3NktktUHBSHe5z4WssGoba2QeOTq7R7u77KWNEuHKI0k06HR1q4fv8ByXyXmZkuZ8+dZHVti4k45uc71JXg8oz+5g43b95nfXdArhWTwYj1NOOj/uQr5+Q39iS1U2gRIi040WD8gnRGMXCe/6JlFFsjwTqoa02z20E0NJttepMJK+JoKwEskQK1u43OWujcMr5xi50f/5DFv/2HaBIQjZbgXGi/4ZWIRzgo/LIIJ+oFsdby8iL/+d/7u3z7rW/w8Ufvc+3GbbY2d8jSEbkTIivUoxgTC7Mzs5w9dYoXXnies6fO05npIijPW+OCcigNmmDoKHxungvqWDty59i5cY/0/gPQGmsiJipi2B+SJQnzF88TJU0kSlDlCaJfXF4BSyA5DnDNwFpuInOgp4rrA+GHV2xI4wkVeFDhhEEFZSdYHAPn76nVFf2Jh5UlSUStUcdtQ6PTYtIbcGe3zyGliNAYBGMtWnnmcbW+w+6/+BPqh44THT0ZJLUjzy3ZJKXZaoQ8U4dYQRs1Vb4SjGTlDYVWd5b27AxRlGCd5v0PbzM3J5w6exYw05PYUmoFAzsoPxfmqzwlCoamKOXToh6t0/vRX6L7GxDnZcpUK4f49CGSpWNeENRr2F2HkRSUD7ApNS3bq/AK2xMza+q1OrW4dmBzCJ54t7swz9rKU7ozszSbDdrtFqPhkGY7Z5Kn5FnKZLCFnuvjTBvwcMXBMKNbN0zSnN3WPGudZeq7qxhxgRwNsn6PJ//q39A8fgIzNw9OsCHxVnmLxCOiIjxzvvOBGnEFdwzkzgY0ThCTBeGX0iFYEAxMJ1hx/jBECKArD/su5l9XTitAyEYTnLPEjbpXyjbjyb/810zu3fIGWPEepT0sWgQaGvW8o9v181aiVGDqhIpP7fEwSGEmUjQaluW5J9xZ2eCPbmQc9FWaoxUlU1hXpR1ajE34RhASTFW0hxNnzQbJqy/RX31C8vA2sVhUlqLv3SP9y58Sd2fJjh/36KNiPEsHf5pyEscRly6cZ67993j+5CE+eOdXPFrdZuw0GcH5Fr/GI1E0lTAX55xZ1Lz22hHOvfkqjRNvIslxhFopR6e+kg+8K6txD1L48Q3qt39NZJ+ASrHaMl6G/Fuvsls/ibMRxann1DRRZXuLMQBKPVDMq7drypE78Ks6jFL5fV+z9v2411B1ocTjtKoeQUaDbUZkF44z3n4e/XmP2ngdLYJbH2P/6hNPfPjG98nnzmJ1bdrHMK+F06kVdBO4fDjhzNJxtl46Sn+SkeU+Dzw2mkai6NQVzdinFCpn9wSfpmMZZEA2xj28S/rDn9C8coMk9caR0zG2u0j64gv0jh+loFGVfae708Bk1XmcQpeLz9x0qL6WOfTPrayRyr6rSInpnbL3e3tNwUoQR0Eaa2qvP0+2uk52/SMSRmAF82yXyZ//lKjWIH7122RxCykXbuEkU/6ulWOxHfNbb13g+fPHuHb7TT766ApP7t5jvLODzXOU1jSadZYPL3Dm3AkuXjrN8SMLLM60qUWRb2PYOq5suZoG+Xb7DH/4U1rvvUd9uAN4nSBxzOT4Meybr7IT10vS1Ola++Ks7AtflaNbRYZ8HVfRlbLs8p6/SeUute/TgJguBl1RIoJLpLU25EuLjF99GdXfYe7ZqtctWYp5cA/7o5xYQL30EpmJ/NjpYFcFRxwl3gERRRzFzMzO0+3McPbcGbI0D6n7gPan10kUEYUqokUgqDg8UUUlNanIxixn8tFnJH/5U9pPn2BsCNigyaKE0fnz9M+dYVAhrq0G0QvdTLmmS01NKVgrcImvJ214esn+xTJtqG9CeSA0/WPxWx7X4Nhp9MuX6f/iQ7pWgR2je0+RKx+jZ+ZIvv08ab2qS6t2pJR91FGN+SNn+OZv/w71WswvfvEBT7Zz0mA/i1gQO9XTYYmp4OA2Y83FE4t87/tvc+Gll2m0ZyrycHqvqshGPZow/tn7HfIBVgAAsEZJREFUtG9cI5n0yodmWiPPn2X36FHGmFICVUeqPLyo7M1CilfODfaO6X+gq7C/9ymrfX/345rHBn18mfzkGSY7a2gZersjtXD3ETL3OXRnMCcu4HS8Nz4DVYBf+XmkFUcX2vz2b73J+pO7bDy6CZKGsfe2vFhLb3eX/uefcP2zazipIxKjxAIjtKSUdVcV1DQcayl+++0zXPzW95DaMop4b1uYNs7vZY+yST98QvzBh9R27wSUjZcBecvgXjzJYPEMbqxATVGx7Pm3QIQe/F5UeCqENE0Zp2npK6rAu2pCwKZei6klMUlkfEaO80VLVKjyWgArir1hRUiVD9zk1nPQxoMBjfc/wc7OwIULZHEccHzFAYkK3ETenjSx4fzz5xiOJ/zlT99l5dka1k2wZcl2L2+lEOQhCKQwODFgIqJ6m2PPneWv/+G3uXh4mRp6DwJ0qg6EuN8n/vXnzDxeI3I27KNA4C6aLKqztTDD9Y11rt9/xLwcxrRrzLY6LEwa7I7GdJIEbM5oPOLWzftcu7/KwHnbOLeKzLqy2MOXXb9x0Ea0QTlHpIRUgWjliVetw2WW3EIzSVidTLC5Q2tNt90GNLVGHdtp8WCnz3KcULcOqwUZDtCDka8KIoqdH/+CmUuXqL/8CohFZAotLQ1RBzm+ilRkJGwz7SNzOJLIcPb0aY4dXuQ739xiPM6YTFLGo5RxltJstqjFMc1Ok5nODEktKRF0FvGkeuHoQlxwULX2qY9K0EXKVljAw51dNn/5c6TfR2FQuTBcfcrqZJf48ALt44fRShHrGtr6NuIcylo0Lji7HhWgXDjxNwZlKvXpD+AS8QGZIjhU+mrBqbbiu+2UYmS1D7wlMBw7sjzHRAn1RsOXazaK7tI8K71dntVjOjnEhKiysxgRYkCu32Hzf/gjFv43/wXR7AxKNFmas/X4MY3zp0EX0HDBFSl2gULI2+3F6bwnNJZQQWGuVSPrrQdfsziZVKXcqnJJFAEbcbJH2YNHGKSjnK0/fxf94DrOpbhcwChyp5D+iMOXLmFiX97UxAlKckR5ga3jBk4F4Rwo6ovymUopklqNJGkc2ByG2aJZa5AcPcrKyiqNOGZhYZ5+v8ckS6k3mozHY9J+H/obmG4TF3gvsvEE1WgSRzDG8HjxFAvDdTpZinYeVaPF0bt2had/9mcc+3t/D4lrqMD75KPvGZFRaKdDiphDmaiMXBMCH0Z5gwLlI9ci4teO0mWwrFA5hWFcnKL5E+GA8AkkZiiF2Bwrjqhep6hQtfHzX7L14z8PhHPTvFcnPuKutUKOamZPe/JqHxStlCFS/iREgjEcia+E57TCRIp6JHTrE37y7kEHbSp7u/QjvsSSKgzB0tJR0/VeuZxSZIuz6LfeoLezTXdnDYNDjUeYq1dIo5ja3/ybpIeP+KB3YbQTTsOCFS+AMoajx06yND/Diy89x/3rn/Lwzi22N7ZIxxNwOc1YMd+tcXi5y6FTy8wfP0Zz+SyqcxLULEri0K69p+w+/qewjzLkRzdpXfklUXYH9AinHJMZyL55ia3ll8hso3Iy/r9kXX6VrJx+/nXYp5U4G/s5UaotKp358JM39qczy5fci1LIXJP0xUuo/hh9630S1wML6ukQ97OP0KMB0dvfQ5afw+kW06iVN0GrQReFr+rVaCukFZeGh9+yrkw13ANZl2m6kNe/FoY9shtXyX/8U1oPVqjlAe2hNK4+S/r884wuX2BoIkrOjOAAFkg9wSscKRMTqASGwppUKlR2qDhwVVjOAV2lbS+VNBGZpkyoavuq3xP/z1eazEox7tTR3/8Gu70tZlfvYBijnKP2dJPxH/8JiYlJXn6LTDegsC1CVaaidSH7nnqkObHc4ejiRX7rzUuMrTCcpL7qJlCLNa16RKQDCbvzKTrecK1wgoW2FXKZfo/Rj39B81e/ojHY9raXUlitGc/Mor77FuvNDtapSrumZI7TtNW93S+AgeXvhTFd4igP7irWcvVt01mTPZ+X/wYn4AtMRiUy1P9cElBHNbKTJ4lee4Pez35KZ9BHi0NnKcnD++T/6l8SD4bob73t+WJcYZP4ILwjcLqVa1ohRmFMjImndoSgQmC8kBWUw12MoMOnmOlwnxqPGf7qA5Kf/pROfwPjMhBvM2WRYffMaeTb36RvYu/AfGFoVOX5EtKMv2qwizH8eoM2X3p5g3DPzwVPntc0xQLX2OYM9tJlhqurJHeekViLzgeo9Xvo99/FNhKi106R1XXZJz8ALhCUBjtfQJkac8tnefN7Nebn5vj848+482iTzUHGxLpQ5th/XxtFLTG06xHLc00uXjzJ5Zdf5dDxiySNWVBm2nzZt2oF1HDM8Jef0vz0YxqDjcArpXFakx47xOTlV+klDXxIbv/6rjyIimkRfpveqcrfv45ZnMoaNUXYVP4t9sVeOVeNM/lUyrxTg+fPMXxwl9r2I49yt4La6eOu3UCaDaJmG1k8hmepLN7P3mEJ/gQKarHiledO0P+DH/Bnfzxmc+0RLlQVksIOckVoOwMGgQpj+lCtxNNNKOFYW/HX3jrGG7/71zDdM4gkFMiwQgtXA5wq2Ar5zR72z96ns32DWLZQZAgOW4Px4Q725VfZTZuVIaocGoS1X0ViH+Tli1DkiIr8oav2HKQminxAOUlIEk1sEpqNhHocEWlFnuXkOGpJRKtZIx84X01aSnWBVppMR6SRJncZEytkeU7j6Tr5X72HNOpw6hSjLGe3P6A9N0ccxVOfDr81k2bC62+/RKPd4Gc//TVPVtcYDEe+Mp/ymSRSkIqLRojQSZ1GvUN7fpbXv/MC337jEh2tMc4jZlxxiFTYOyKYLCX+9BZzV+7RzCbTXVPYCdowmG3zIIFP7z1gZXOLfqxod5u04hpJYmCYgbIYsdy4epN3PrjGjhNU5NFJkdHEsfFl0b/i+s05bZQi196oEO35O7QnhSC1Qp75iPI406STHASarVaYbKGzMMvGTo+nSliIYowEAlwNgsYojVvfYOuf/gsahw4THTlWLtDJcEQ6GdGem/N+lvhoqE9zmZqlIs7HKZ1fGDMzsyws1NA6ZjAa8857v+K55y/Rarbw3pAQ8M64AIv0YZVgXoR684XIEfHBqkJvZKMh2+99wuiTq6hJjjYGR44RS0Np2mfPErU6HrqaJFjlS8Qp5XP/XIEsUH4VlnXg8WXIlfrqqNv/P1cwyUP6icfZRMqQI6WS0gKTVJHnQqemeLoj2FxIEk2r2QQRrLPUOi16szPcXt9lwRnmxBEjfmEp7wQoa8ne+TU783Ms/IP/BGm2iZOY5mwbX51GvASTKfTPicUEc6Ug3tLhtKnYJadPHwJZoHBgpx2cKirr/FgWxpwEDgD/iOD0OmH744dMfvUOyo187Dx3iNOk2hEdWaR54gzgy4brKAYVo0yEsyk66SJ2msYmhAopIh4qmMTU6q0DnUPfJ4WJIpYPH2JrY5PttTWWl5dAaSbjCeN6nXQyIRusEXUOgapRoMiGg5RWopjkjp3WAiudQzS2HhOR+zFXIDZj5c/+lNryMkvf/y1UOR9+LpxznmC6sJ1CMKaAh8QmCfcXDCqU8HWlywQ5wAtUcQ7FdBy9EeZRaIpQSSpUl4mSCK0EscLg+g1W/z//BBn1MBX6fH+/l1muG2FeFaKWh3qG0F95aFFAV4sWlbnGIX3Kk3crDjjLzY9FqWkLZay+YChL9Z8K/KZc6SX6AazRZKdPoL/5Fv1f/JTWYBcjimgyQT79hPEkJ/6DP4TTx7HGYAtkRzE51UYpiBtdDp95kaXjZ3n1uzu4dAvJ+ojL0JEmqjWJkg66NoPVTUQSCNWHprZFUHEFeiIz5Pcn8Jc3aX36C2rZNZQaIDgmDWH00mF2z73G2C1QPUPcY2yWgxIks6rcIcUHUt7+dV17nPVCL1X9vi+cdLLHeZ4+aHoKVchoxMtoG4E70kG9/QpkOXLvIyK3A1ah1ya4n1+BzQ3022+jT7+GrS/iVBwAoq4MfgCe8ybMQ5HT7YfTB1F90KZYT4VpEpwWEZSb4DZWSD/8EPXuR7SfbWMEj2pTEbbRJX/+MsNXX6LXbJQho2lXXYWjSpUBj1IalDdO048KQ3oapDz4Ca2Gharzuaft+5yronV7qr4VeiDwi6gwNsPFWerffpPdnwxpbz0hkgyxjmR1l8m/+mOS8ZD45bfJm3MoAkqj+uLCUQhvVtrz0dW1YrZWY8q3JSVypipzv3D4E+Qp1uLW10l/+S61d96j3t/0864URJqsO4t96zV2l5aZuOn4F85XgUzZ6whWkqFEIapA80r57b3opQO69szPXm+tqEA0bVnxf9/2Ank4dfi8dRSI8VCVf22SMLl4HhkN4P33aA+HntNNLMnWJtm/+dfwdIXGd7/LeG4BG0d+yzlfgrvUUUXrFJVUs2LeA5W0sI+LQUKaOSUJMVmOXVsne+c96r/+kO6oj1b+IBOlSaM643PnUH/wA9ajyJN67hm2siXe/mOqt0thFuSrwN4Nsue/X+MVbDb/YzWJZt9M75epOiKdO4S8+CL93nt0n/WIxaLzPvHTG/DLGGs0cvkEecsXJSmCWSXYT/zc+0BbQnvuGJffbHP87AWePrrLysOHbGxsMxmnOAc6img268wvzHHo6CEWjx6nPbtM0pjDmHrlEFrKZSjBrlUC9MZkH16l/qt3aG4/QUuKUp6bL53tkL3xKpvdWbKKbiwGZKpXvjgnFVNjen9VlR7oVdgl1Z2zv1XV1FL2dKX0uJTCRRpOHGJy4Tzjj7doTnoosZjcwfoW+ZVruHab+K020pqt7O/iNWXJQv+88HGzHvPWGy+ileXHf/YnrD1+RJ6nlLZV5XBAFcI0pF5qDQnCXCycW27yxjfO8uJ3vkl98QUcLUR8Fkh1a03776MO8mxM/uef0F39lNg+A3x6j9WObKGFvXyOXusoziZT5JcflNIuZvp4yiIHB3SJwDjLaNXjgIb3qJo4jomTyNNlJBGNOKZZjzEKTx2iwGhQiWGu20aA3cGoLAijtEZpQ24ME60hs+ROSMUizlJ/uIr6xXveDjp6hHa3jTYF2n5v4qoAKop56dXLnDh1nM8/u829eytsbfUZT8ZkmfWcNUlErd6g1ekwuzzLsdOHuXD2GDO1GJMLyqpSOwh4EEl4jxIFayOix0Oi1ix20iOqtEWUkDaabJ48yvv373JnZY2xAd0f0x+k5AWPqy6qKyqerG6yOUkhiX3BH2N8ellk0PqAqkc5BbmDXHkyW4VgQhqRMzB2QuqEZl0xFCHLHdoJrXYb0b7SS6PeIpqdYWVri2ORYSk3JEahxE+0FUdshOHtu6z+03/GkX/8jzHdubINva2ntGZmEB3qaynIrUWL8QZoKQAJBEHGO1/a13KPk5h2vU42HkGjWQp4VdmkRd69j7i5Mn9dQvqIU/7hSnxFm979B2z96C+wox6CQ2MRo0lHE7SOWTx7Hq1jX6koihA7QbsE0LgoQRW8HgFJ4hyeIjUQEesDDdr4Ut+E00QVBJBTNhiJgXhKIM0cmYVGrJmkPi+wrjXdTgctGuUUo9RRW1rmye6Ij6xwMYo5agUzSUEp7xC6HMks/T//EfFcl+7f+I8gqhHFCaXRJxKMTiktYWerbfStF29deSPUGKrqW5ieWguEikVBfBdCTvn+e/4Aj3DqP9hg55//KWb3MWiHyi3WWVIUWV2x9NpLmHq31K8mSbASYUwMdgKxgzxDXD51eAuBKj4g0WodLNJGxEel8zzDRBGLCwtExvDg0X2atRrHjh0htxmT8ZhsOESGm6jmIYI5xijNaDUbJConVZrHc6eY76+z6CyCRTlfntuNRjz+p/+MeHaOzksvoZWnb/bySgcETICAhzny5GQh3SPyJHtird8/hUFf7Dttytnzy9Ij6zyqpjDA/ILQgBULWmNUDALDu7d5+H/9b8jXHlOcZXhkXoD+a4VOIswLEd3DqefhKtKzlNtjVJTVDNRUGeqgqJX26/Nr4z8t5zWMxf+C6i3VxRc4dvzA5vUa+tIF2N1Ff/wBjdEALYKZTFBXPycbZpjf/m3MC5dwzUpKDUUqilR+BjCYuINJ2sARCiPMBQPI/9cTQuPcHsepgIAXwQI7MbhrfczPr9G4+Q5xegsY4BBsTZicn2Hw/EuM4+PgohI9J+Va2Nvb6WjttTpVZRi9wV0Ygf/Oof3Nr4p96E94i9Pqot1u2oYv/XrZSErpFdZ+iZFRIJFicnQG+f5buLhG4+5HxNm61527Fj5aQVb/HF65S/zi67ils+TxDLmKgkNQDmJZ8aBM7QneSZnyqwonMTiISkBS3GALuXOb7NcfEd28T20wDmTeGqcibGuO7NJzjN54if5MmyzM0NQ9pvy3+H2/C13+TfzfqsGGosz2lL/ia7zK5TRdXWUfKoG4Mm2FwvGq3uj/qxSQaPIzR1G9Nxm9/y6N7RXvjDlHsrJF/q//FLO2TvT6W6jlE9i4hmA8r59QEuIWFqV/hQ90mYLfJKApS31aBlSkDHoX6WciDjcc4R49IXvnfWqff0pjuIvCIcpDx/PuHOlrLzO4/DwDZUrnbtq9KadK2bSqDq54icVwVCsOKfkCC8W/11WVAKVoR/CwtNBeKZLCQjtL1FfoW7l/iw+mVkbZZ6XIGg3khRd8YYVPPqXR28Y4nyITjXrov/oFkyePiL/xBubsOfK5WayZ8olVW1ugbqqpdVNnd+pKePXkkW/KAXkOO7vYO/fgg49oPbhNPR2F/eFPlfNmi9HZ00y+9022k5onkCYwGu3x1vftMyiFZdHvva2m/HT/Z1/LJdUfq85rVW5UU7ume9LGDdTxs4wv7xJNPqe1MyKSHJ3tYJ5cgV8pJLNw+QR2JgnVZP0jSvCNTOdG6Zi4Oc98rcPM0inOvtAnT0e43CNxtTbouEYUNzG1BjqqUXBWidJ7xnGaDuV1pNscIB/fIHnvXZqbDzBuHHqsyVp1Ri9cYPfoCcY6oljJ+4bnC9d+STnlfZv27+sTp8V6r66UL7ZWmOrNAjBf2vMK8kZC7eXL7Dx8QPxsQpKPQUBPcszKOvkHH+MabWpvfodUJ3vfIIFWQ3lUW3EppZhpN3jjtRdY6Nb5/KMPuH71Cutr66SZRzZ52Rl6oASDUJecuRqcPNTiwoVjnH3+AofOXKI5fwpUF8SUNktpC5TyyO9d2crJfnaT5q0PSLJHwASUkCshn01ITx9mePwsI9pVdVPqzAKVXvRPqjcc0CXiyCYpWS1GGU0SxyQoklpCvZ7QaDSo12ISrYiNQpzFis9O8dw1mnotYb7TxjlhMJlQLjQlZAjDOMJmOc45UpszcTl1G9G8fd/brG++jD51kjSgTwQPdvDrQ5VmuxNhZm6Gb3//DV5/M2Vru0d/kJKmXvabJKbZaTE706VZr/nCCqFSeMnRKQLisNYf+BYpx+SONOqiXnkR8/AuM++t0rZTP9rGCf2Th/l4MuCD23fZFQuRHzMrOU4s2CLV3Ge0jLMc0Zo4iqjVEuLYV4suOEq/6vrNz41VhkehaGLxTs1Ee0JZJ8LIWuqRRjnHcJJhFHQaDQwRzirS1NGdm2e91+Ouc8TaMWcVNdEYNFr5UzhnHdu/fJ94cZHFv/+fo2tNosTQmZnzSqwSZfQpF4GjosiLEA/txGjPhxGIDiMNL7/4AlGS+JOXsJGniN8QtBFh+r+gOqWi2IP7m/X67P7FX2EfPQAsTsBasFhyA60LZ2nOLaKVxqociWJUrtAqwjoLtS4esqVKZIElkCFqIVIR+sA9xamiK6vu+eNADCZwEjomDsYZ1BPN0AqZ9eO+tDBHp9VBk/Bs5DCtFvncPNc2N+g1a1BrcObZViD3AlGCxaLHQ7b/xb9FRJj5g98nqSWU2Glx/vBI60pQ3J9IOQm8KEVkU1GewPp4T2GUTk3nIjDgbTaZCs3ifgGxjtHTPs/+6b+FRx8jjL2S0L4AelqLiJ4/Sf3SJVCmHCyVxIhOUMqgXIbGovI+uJlCsvvgVwCdaK2p1ZMDnUERyPMM5yxkDhPHtDsdjh0/ye1bN+jt9mi3mkTmMCurqwyHGyTNRSYYrBJyUQzGjmZimOQ5/WaXB/Mn6azeoBUsFUHQYsl2Nnn43/63nPnf/W9pPn+ZXDwRdxxFOKdCIEx8tZeQ1ubRNPhUP6VC2pEuc4bFeYRckauP82gA5zyBtVPOV28LZtiUxtJ/F2UY3L7No//zf419eIfiOCNnKnwFhWvWcBcSZi6PMKYs9Bem0q8lyxQZpsRzdqFCWoQSj/gqTvwPfCt+UcN+dVBB9jiIX/AM9zhPiqzTRl5+ESZD1JUr1Ce+goXOJ0R3r2MHQ3i6RfzWq9iFTiBbd3uqD1XDDdMTvArPlgRD2XllN22pQ4kuAzaeH8xgNxX5h89I3v2Y5uqHxO4xMMZpsLFjcqbF6LUXGc1dwNEIylSVsrnqOO0lzdwfuAl3fhlx9AF7GFJ9pEznr2xrGQwvXVWmeA5vxha8DHtcNuWDK2WTtUJiSI+0cd96Bakn1G5+RG20irYpDEE/GODWPyO/dh/13EnMhcuopdO45gJiGrgKPWwZIBS8EVlyQYV5FAc2h3SI622inj4k/fQz1PUHJLtjIuvToZwyKFXDduZJn3+O0cvPM5ztkEp1Xvy7ZM9v0/d7A7SyltU0TXLvFZCuX4OD8WXLQlVSSPbOzf5+lFqn7PAUN1qgcjV5s4567gxkGXwCja0VjKQYAdb72B//AvvgEeYb3yA6dwnbXYCoTpEkU1gkWrwrX0XUaLwenRJ+B3SSBJkYPA8lAuMxbnOdybWbqE8/J3nwgHo2DvIlxpmEfGGB9MVLjF98nkFcZ68jXBk3pcq27Qm+FYiRMJdhpQeHrBLY/Lqur3h00YMycUqYBsQqwae93y/msFh8PlyWtVrI5csIGnflc2pba0Qu98H+fExy5xb5+hpy6gzR+QuYUydwc3O4Wh0xVXSu/9dVT/bDBhBUOadKHJJb1GQCuwPyh4/g5i2iO3ep724TE0qzo7A6wnbnSC+eY/Tyi/SanbIeR9HL6f5U+/bm3oGYjtY03awgBf26rpKrRqZ778tKVZctqiCUwhModKIixrUXyC48z2gyQX12neZuSiw5Kt1BP/wck2fI4DXUS2dxSy2smWIEnUyxSMWTEY3SCaYWY2odakVLwvpGVGXUZHoCEtabRw5Om6usQ1a2cR9eofb5x9Q3H6LdyL9fKfJmwui50wwuXWRQawaraO9Y7BucvUPxFfcWB5pf51X6TV8qN4tx+KI+L+8XhdWQLc+hv/EqvZ/s0u1vEGN9RsUwxTx8Sv5X7yCNFrVX3mAsptRpX4h6hMun0mvm2m0az13i8NI8L734PCuP77P66D6bz1aZ9HaxNiNC06xFLC11OHxkkeXDh5hfWqYzf4jm7DJxYx4xdQQzPZQsDIKiQ4Hbym7n2PceUvvoI+qD2ygZAD4dL29q0uMLTM6eZdw+gpN4T9OL7uwbsimK+QAvEYiM56SJo4gkSahhSJKEdqtBt9Ui0iAu9zZ9CNZMRimjPKfeqFOPNfVazOJcF7Z2GaVZ2RknPmgzrifYSUZuLc46JBLizNK88wgzTlG7fYaXLpB2uiWFwTRpuVjnPktFaUOnO8fcwiGMSYIe9Ie5hVHrwuEiAnmWef1qUyaTFAGsFSJjSDOII4NYjdTapLU6k/v3ULacXWxkGBxe5E63wc/eeZeHO9tY4zkBNZBnlixQyigngW5FITkktRqtbodup0mjUSMOZM6L8/N8+NFnXzonv1l6lHhFokXjlBC7yimrgjER41ToNhQ4YZRZnAjNZoM40RitmFjI8Sc41zfWmbQSLqkaR/ojdHDYlBicctjMsfbDH6PnZln8/b+OjmNUFOP8tpgqOOU9ROcqEDjAKn9iqAXP5A7ghCiO0Np33QYulBIj6P09SgLUkAZCOLlAVDCOhHw0Zv0v32Xw/vsY5bBa4ayQCWROwcIC889fRseRh7cq0HGMzRRaR9h8iNRnybOcGAnVvzXahsCVAmPUgSJtJNhuUSCHUiEQYYtjKfGndRZHDowzoZkYhqIYj30QbnFxlqTRYWvYZzK7RDw/j3EGyYWV/g7vxzG1IwucebxOhCsJjxUCgx12/6d/Rb7To/kHv4taWiQxpjSIptWdfHKa77v4NCcnIf0GijQyX3o6KMlCvof7ipP2IrADUqbDiHUMV3o8+x9+iP34XSKToRoxdpQiDk+Q1Yo59L1vYprzfu49ZhhtNC6KveHpbCj35sAFDgIBZ3NEHNooIm1o1DsHNofgUURpOvE8SyKBR0DRqNc5cvQoWfqAtfV1xAmz3Q5qnDLIx7goQtBYEQbjlKVuHUNOpgyr86dYHGxxovcMrexUz4hjtP6U2/+P/ycn/8v/gval5/yc2Axj4sC/4IfIuSLdzVeXK9BphVEPzgstrVDaUJwQSNBAKkyYsw7EYUyElAEUhcZgbU7v2lXu/9//G/KHdzGhcopSYJCwlgWMJu9a5l4eE9VseVriUXLehtL4ksguzO30EMZrPxMM1iJ/9sBdDKmcePHvCthUv1P5QuXDPR8rsFojC3O4117DTSao2zdIJp5sT9sxavUe9sc98ocPMd/+JurcSWzNTNNCmSLXypcGHSC+pr1fI06Cgx1OpFRA7ys/wmI1MtKkt4bwy7s073xMfXQNI5s4T2uMTYTxqQbDN15gdOhFcjWHiKk4FbK/h1AxNgtHsUiBKG/ZM3BfU+CtsKTU1HHwTau0LaT5lT0pO+a/XKxf/1EwQgqHTiohHiW4SJEebeKS53FNg7v+Kcn2E18O3Aq651DXtrH3tnAfXoezhzGnzsDySWxnAWl0UHEDpSL2sDQG/hPJU1Q6wg12cJtr2CdPcHfvI3ce0xhZj+pQAdWhYpxpIPNLZM9fYHz5PKNum3zfCfjUFS3NrPB3tWesyhPE4t7KXO0pUeuqTzyoq1hPFUN7z7r/sq8UzpmEr0/nvDgo8J/4anhKKbKZFvLCRazS2M8/prnxmMiOfRrm0GI/uYN7soY9fwXOX0CfOouaW0LqbZwxhOMoSmMlrCerXOWz6o7xzj7W4oZD7PoG9t593I0bmAcPSPp94mDvWAwS18kPHSF98XlGF08zbranKW5lUGHKM1QM0DRgMw2G6MqYFs7+lEvla9iLxbiEsVdBGE6RI2EdFQK9HB+mglPY+/0gU6bHd8U78JVUZzqMX3yOSc1Qv3qF1rNV4snEV05xFrO9Abs7uPt3cIcOwbETqGPHYXER3W5BrY5O4pAyI56IHxUI/gVlLWQZMprgBgPc5gb28WPMo1WitafEuztEzlY4eTQurpEvLjO5dInRxXOMOh1febUqQqdLe880lCkIwhQlWemzHyZVmt5SIKQP7FLl+/e99QtXYTdO+1FKyso3vWwTHWNnDjF+/gWcTZFPb9EeOJSkqPEW+vEVolHPj++rl+HMIjaZBo6KkukFP4tUlq8PjPpTuimKRcr9sjetoxLAL4ZtlJPdfIL69SfU712nNlzHuFCFD8iTmPG5EwxeuEy/u4hTU9dtbwWoqsT96qv61/+QJb+/NHayby0W+7FcqmVgU8hiTe3iWSZPVhne+JzGcIfYOY8MH06Quw/Jfv5zTK1JdOYiWRyXO7YMuu2xafyu0UrRrNdpHjvBkUPLnDt3mu31VXpba6TDPrgUozS1OGJmts3swhytuUWfEq7qWGe8B7dHr091gJfLCnKH20rJrzzFvPcx9fXrGNmloIXPI0iXO4xPnWC0dJJMtyn55CpNL+zkL1zlwjq4y3OtGBr1hMOHlsmUJq7HNOsJkQg2T7G5I00DWmaUstMbMnYeWR8HsuJmvcbibIfNnSG5BJsIzURrRu0WedYjd4J1uffXtSLKLObxM8xwRLSxxeilS0yOHiVTcbD1Kr6xeD8xSjo0uoskkecUKg7o03HGZJwxSTPvn0Q+JWk0Sak1Y8/lqXyaFk4xTFPyzKJbNWr1CCVQn4yINvvUrJ9PpxWj+XkeH17kL65c5bP7DxkpwWiDwfODeiSH8yh/kYC4UahIMTM/w9zCHHPdNrU4xmhothscO7T4lfPxGyNtFAblIpRxTFQearV7dIYB8gzijudpGecOK4pGs8PS/Bzj0YRBBq4zi+4oRv0RdyVD2jXqScyh9TUipQGL0j5Fx/WGPP1n/wIEln7v94ijJBgo4URcpBJhLNJhFFZ8BSOBkA4TymuHUokSSk97/htQWJzPe/KZzDI1rP0zgmktgPh0n50PP2Hnz/4EGQZWdwErOVZr0mab2VdfpbF4KDh9PgCR1FrkIw3KoCQnEkue9RE3E8RHoGTUyhPRazjYnIxgQgbi46ky9qgDF/h7XDCoJhNhbkaTCgzHGU4sjWaTiTM8E0P92CmIEkRFENfY/uxDxts71I4sUz+yyInVtVK56TBnMh7S+7Mf0V95yvI/+k+JT5/1Dnxp9AZFV3XUCjudkBMpIVBa8g0FoVgo/PB5cdpX2GPOWsQ6tu+ssfk//Dn26rtkDHm4NMPy29+g8e5N8scPybSj/eolmqfOQ8HNIR6nobRBmRhEQqqZD345m5Un6s6WZjWRiWg2mgc4h/7JW9vbaKDRbKIjS55mxJGh0WjQ7XbJspTNnR22trdxTojYJO80S9Mhzy39iaWWKNKJIzMxt49cppUOWB72iQsjNYx3+vQx9/5v/w0n/rP/jNk330RHSRjUYo4CWF98OXnrrEdIqeKUOIyJCqe/TnDYkOZWPCLMpwpIjdyijQYdUpqcY+ed93j83/2/mKw9QYkjEgkx14C2Un7PbzUNC29pWp0MpRzKecQPLlScU4FAOQRsStMz7HsFIQfXOx+F43KQ19Rh/TJjS33J73sdpf3f2KPLNYiKyA8tI996iwxL9+ZtGtnYBzZdhumvoz95l/TuQ3j+NaI3XkYdnydvGsSoMFfe3JTpNPu5Dk5bke4w/btCrMaJQkaQPxohHz2h9vlVar0rGFkFNcYpB0pwsTA53WTwzRcZHn2VXM0jrjiFlopMmL6/OIUunURhKisqo1MY2V//5cenTF8X9p5gyt65KhMN9k1x6RCVH3zJulAgBsZzCdun5qjZU8w97VJbe0I83MZIBuIwI0HfH+Ge3MZ9+AA320YWuui5WaTTgUYD4gQCzF6sxY0myHCIbO8iW7vIxi5qa0At9WhHVRLYG5yuYZtd0iOHGZ87zujIIjTjogZPZUkXHAJeVrrQrz0lofeOAEUQSVU+L/bK/l1xEJeU67cIt+zfk2rf7/6wqrq6inaVMbfyG2EVFrLJKPLZJrx8nrRmcFdq1FcekKRDlFjPr7W+i2xfwd18gD3yKZw+CcdPwPJhzOw8qtZETARKBYRpQMMFtJsmnOrZHBkOyDe3sM/WkEePUfcfYZ49ozbsE7m87LFVMa7RJj9xnOyly4xPHGdSq5X7vPBF9syZmo7VXhzVdEz2LvJKopF8RSDs3/OqUn6W/tK0ufsbVxGY0/mVL95UPK0aCvCBVq3I2m3WlxaR4WmOdbq0Hz2m1tshKmwGmxNtb+J2t1H37mPbHWR2Dje/AHNz6JkuUkuQOAJjQGmwDiYpDIfIzi5ucwvZ2kTv7hCP+pg0xYRgDWgc2h+gthpMjh1lcPYMk+PHkXYHUZU05NJW2r/GpexxtSR6cbhRvVQV7va1+fwhWP2FNbJPJu7ZcFMnudAb1VRKiWvkC8fg5fDJh9dpTQRNjkp7mGe3Mb0t1OoKXH4F/dJZ8qUaTmWVUJCUaFSpvLJEAwXZX3LEVRdg8a1wjxZNvrpL9t5Vatc/o771CJP2QPLAAaXJk4jx+ZMMX32dwfxRMv3lJKXValHl61Sxb0N79wTh/kNd+9+2d8GUwd8qcrZi61eDSqIgbzcwr77IaDJA3b+DGvYxASFsxinu6g1SHRM5hTl+Etto4rQuDw9LRGdIjy85fYrKpyZmZm6ZuflltMbbjkr8IZUjIDX8HrHiOSKn1mOFravoQ/hUJha7NsRdf4r+9RWajz8jljUkpG5aI6QLDdIzxxkfPc2kNu8rHJVPofIOvmA7FEjog4zBCWCdLxBTTxIuXjiOjmNfhER77tP1tTU2N0cQAiNbOwN64wmiFb3RmDg2tJt1UEKzkdBsNqk1GrgQZNFGMzGa0SgjF+0DN4EEHzQ6dzS2dok+ukr8+Cmj1y77A4VGK1TdnCLalISMFTEghmw0YTgcYpKENPdkwaaVECcRcWLIJxkz9ZqvFLYxRnRClgloQ9JuUldQq8VERmO0oW6bmJk5rBg0hkmnxerxRe7UhHvra/RSi4s1OhT3sQiTLMfmvsKOc5Ysy8FoZhdmEDHMLczSrCWe365uOHxonlbtgKpHFY6X0hYlmjxEe1URQVIwyRyJibBKsT3ycKlGvU7SbPNoa0A6t4xZOMz/l7o/f7YkyfL7sM9x94i4y9vfyz2ztl6ru6d7eqZnAA4BGAaiuMkk0UwyUTAaKZnpX5J+kNG0mslkNJAUSUEgABIgQRHLDGbBTHdXdXXXkpX78vZ3t4hw96Mf3CNuvJdZPeyZTNLkZZXvLnHvDd+On+V7vsePJhhjmD2+z6ezc9zeHmPdpTo+w0XNDhaBoMTzOc//1t/Cz8/Z+uu/ixxcw7oCzRNrcsWodI+mL+8VNRMwZjRJRDJ5aUCsIjjoIkyDnGI0JpLl2EGLpefTiKr41YrTH/+ck//kP6M+eYmUFuqGGJUArIzBfHCXve/9GtZVSOy2ccTYChNSqTDRxBtifZvRNSnSFUJCK0RMOmzfpGYjIDaiahDt8likG4Z8OGckQhRmdeSmsxQYliuPERiNR8zqQLF/nWK0mXo23UZ9i47GLC+O+ezlS3bfeYftZo/pyTE2RsR26gWor/H/4k94eXKO+V/8W0x/9OtQjdKvd9FKs3YIdEAp1WzMszZwNHZcKdrno4OsS3wP9O3QRo7+8HOe/Qf/KebRJ0DN+Vj4oxj4hkQ+/P4POalnxO2Cu3/lryB2TDIY8i9qSvcxNnnwVVKOuhGDV4+qYo1kYZIyL42zTCajNzeHgIhhc2Obpqm5mM0oygJnC+bzJWJgNBkzXk7Y9B71nrOzM+qzJzC5hrhEUGpUWdUtm9MKqZdEMcxGG/z8xjcZPf4JO/Wyn69U0hza4yM++7/937nx8CG3/rV/lWJnj05hJ2YUmiQiafLOWSMF5FJaY9SYnLPdSaPpt0JnDBlLUr4SysrPZrz4+/8FL//f/ylxfpardq8de5limlaEk8qy+rWKb95eJcdAzn3tDAvVhNbr1oVoSoVKKLMk0zSvr3wX6xSpt9LWUbzL85yVzd4p0RkjVyz97ukrDgDAOfzN28x+619mYSv2v/yU0cUM5zVVYfAzqtPPib/3FP/xnxC/8T3Mh99Abu2jmyO0Mkhh0xpQMtHP+i5CzPcUBfWK1gHOWvT5gviLZ9hffE51+jkuPkFYJC4hlGCUdsvQfLBD/cPvsrr+PbzuJhirAKTylpeMQb08B2u/wHDc1ubJWt0ZmNZvYQojwzmRV+riXJ2eYTJC5zDozai1dbCWcd3jfh0YPIYvFguCafjOr3+brZfvUt7/gvL4GWZ5giVVwqAV5Cxizi7QRzPEPQVnklPODs69KBgfkRjQViFAp9GmE8xkrrWCMN6g3T9gcecWZzd2Oa8MJrbsxIjtkXWdgnx1bl7nFLkyWH30fLDW+sEbplK9ubY2dHr3S36aHQ2djdrZYANHnPRfcGUHSx67oXItmVh6OuKLSti5d4N7WxuUn39JNTvEScCoYFuwRzPiyYLw2SPi1pR4sIce7MPeHrK7jW5uYcYTTFGm3HtVwsrTXlzgT0+JJydwdIwcn2BOzigXC1zbJjLcfINRBLUFcWuf9usfsPr2N2iuXyMW5SVTXvsx6AegH7ikzxguTXfPqTNY0CSeg86ofntR/rwbBxWaulelP8+vpAcN+zT4nnVFos6Z0Y1Fxw2U9L6n5zOenl1Qff97xPfeZfTppxQPH1IuFxSSk1mCYsMSW6/Qk2P0/hdE66CwRDEYaxCbeBiJCq2H1iMhIJmkMyFStb/nkCtqhqKiPdhl8d5dzm7d5GIyobSWsZjMCxUvr81+Lw7WdL/Mhwwj/VCgdIUG1t/w6qn1F21DF8jgJQbn4bAHun5M/mSSo1ecJQKoATfC792DH5YEZ/F/9FM2FgEHSPAwX2G/PIUXj/Gffhv5zrdw37wNexVaRHKZk/XyH/7GpTUtdC7KSyJFSbyMR0vqP/0S+fG/YHpyn7I5R4KnS6kJQFMJ9XffY/kbP2K5fZNgqv6Hez05j0myX4dyczAzX2FDvK4C5ZtuQ62lm7Dh/L22yXBVdZ9Nz70Bbl9Hv/c9VjEiD79ktLjAatobxSrCjz/C+4j9y7+Dfe992NwiFEUfgLwkf7piF9mBk3SZtKdTID9XLe2Kz+QKq5cWATlgI3rZwalpz7PwhKczwkcPsT/+mMnzTxjpS9bEw0q9XdB84zarr32DeucOLdVwOPoZTTqMXnr1rTVNVoxGj3OGnc1JAhLEjt8xsL1V8eKFZxUiRxcLThdLah8S1QXCycUSMYaNcYUrCsaTEZNx1du5quCtY3HzgNXhKa0mLtkkeyIpi0Eol5Hy0Qsmz884/6OfM/qrv8nym+9RO0eICbVjraC+5ej4goP9XWaHhyzOLti8vsv1OzdQFdpVYLFs8B6Oj2fcfOc6NkTitlKMylSxmYhoxPuQaTsEZwxVVIo7d6mlRKfC87vX+PnEcojnO3/p13gwn3F4sSKKpVVLhcU4B05o2oZVG5iYZL/u7u9i1TDdmGJiBGfZ3JkwKcs1RcFr2q9cPUpJqVG2M8TyQW1i8kDMfMS4pOK1Sw8EXGHwOE5swcaNu8SqyqlOlriYc/rsER+fnjLdO+BH3rB5coa2inEmCzwhzlcc/+2/y/zJY27/23+T4t47KSLRHbZZ0YuEdL0k/gwlojFH803yGkJEYkypEAod2VTnWgmdkpqj7BoTiSqqrC5mHP3BH3Lyd/4+7dFz5lXF9g9/QPnFfVZPntCq4Lc3uPYbv065v0dXulFIZTetc4mjxaYUmq7ig1dP52DQkK51krx7bzI9qjvkOmWjR/R16P18EFg1GFWWTaS0AmqYNem90lXU3lHsHCAxCRCxFi3HFFWFtBX1asWD8zMOru/x3RiZnJ2lCLwm3FNy9LXEzz/j+P/4f2b547/M5v/0X6e4eTMb62mjdt7jGLuqT50RkcekM2gMuWx6Rj7TlapcC2c/a3nx//l9zv/e34XTL4k0YGD3B99hRw372zucziIvrm3xwV/7IcXuzeTcEnJqVvo+K4IrxiCKmARXx+QopyjWCm0IvcJonWNzY+ONzWHXrDOMbIVYQ9vUrFYrvG8pqpKiLHE2kVqVVUU1GuPnM3R5gm5M0/yLElqPRkflDCufnBuH27f4WWz53qOP2GzrDBuEtEITUur53/27nP/sZ9z+N/4Ntr//A7QssEZAHCiEmCCAxuUDJpfs7qIdkObLZpRLPg0x1rImisw2o285/+nHPP/P/jbzj38Mvu75GYwmVTQoBIHWCGcjy/ZvOb7+jRYHmXdHBwR0CXmX1V7QTHbaH8JrdSGhzjqH08D794baq8iBy0buUAG8ZMlne6hz/q4P9MHfgVHpTcnHM0exc5vfvLlB/fmXVM9eUC2WuABWPRLmFKefEf7gKf6nfwTX7mBu3iFeO0B2NtDxBK0KKGxCP4lBg0LtibUnzGvkfAnH5/DsEHv4nGrxFBuPQZYp4TITn4eR0ByMWX3zGuHD79Buf4Oom2nspTPrk2B/nRHcGY5JOb88blfHc72a3nLrjYfLv/nL1KouTpSwEUPLPn1TN4cdxL+f93zJPChnrefuwYTZdMxoVLNzvsX4xRnm8BCzOEN0mc8fwaQjGalDXls9hhQkpcYhedTUJJSqZJJpUxJHE/TggOWNPU73tzifTFggBB/Z0MuG1mAV92PREdP2PvDB/F4FnPXOAlmfzf0OfNOGRnfm5wHW/jWTiw+ktTg8iSXfdFecoOe/yWu4azEmh/Cwn4oQreH+0Snx+Ijt3/x1Nm7fo/7oY6rnj6jaJSb6FJAKSrFo0dUp8cUx3nyKViUyroijEVo4Qk6bStWgIrJc4VYr4mqFtB6LYo1Z33Pm21DjCKMJ/sZ1lh+8y/nNG7C9SWndep+haxE9kEO9DgHQpYf0Vin9OF4mjU7neO8YeAsGo77yIN+VrM8UukBj34b9uvzBdWpCd2JcOUfyXlWFs8WS1WTCxcE1HrSRa7t7bL18zujwkHKxxPhA3lVJ0VSw3iNNTtHKZy3Sj3xaK9mH00nEQAQ1iC2IZUWztcHqxnVmt25wtrXFDMH40Kenq6Tzq9tLPfHcYKCuytnO9DfdXIvp143JcuLNn4hXbuGV51dRUIM11CMb1+ce6IBot5OhBkyF37pJ+P5v0U5GrP7Fx+wcLynaVD5dmnNMu6L49AR5+inhT99B33uP+P4tzPUt4tiiThEnCRXfOwAG54/2EwYhEpsIi5b44pT4+WPsZ58zOnyEa44wsYbBWgrG0O6MqL/zAYvv/oB68xrBuHWvhuiTq4/k8sNfNqbdd70tx03U7jzvl9Sle7y0A3XwBsOzPd/noFOtNbj379JqSqPR+1/0jhsByqbFf/Qz2os58sPfwHzzW8j1a+hoDJlSoE/J1dD/non0hWc6lE9XCKHPyNVMJ9Gjl9Z7dd1RSUFFr4TDmvDpIfz0c4ovPmF8cZ9SzlDWnJ/1VFh9eIvVd7/LavcerZlAJq7uRqWzbXod8HXtLWzIF4cnxO0NrIHoczA26/i+rTk6PKVeKccnCw7PVzTR9PIrBGVVe04vlhgRtqaWwiaCXx+0t9kaVU72tjnBsVisCBhiTIElY7rCJgFipGoj1549Y/m3/nOKuzdpf/2b1Pdu0UynaFHSGMM77+7TvDylPjkltg1HX5xz/PgZk4MDGE8Yb2+yOZ0y2hgxqizihcnBBiEEmlVNDMrp+Tmb+ztUudKtBZwtsHducr63T9zx/LQMfHbyEgqHrQp+96//NodnM9qYHE8xBDY3xti6IbYeQpsCpSSnTRUNo40xoDhr2BwVOFEs4Svn41dD2ii4aGklEE1EE+IHaxLQ2aunqQ2lKGWhzJaeGJWqKFP0dHOPMBqDKs46ynLKqqjAWGbLJT+bzzi4scfX6iXlqk1wN2vXueB1S/v7f8rj43Nu/9t/k83v/QB1SaHSLl2KRGaaDubkaOnyg5GchiFCJPbvG41ZaU0OqBhiIlRVJZpkrAcfWByf8PTv/Vec/df/NfHilJmNfGaVSuBf/tf+dV7+R/8xJ75m5zvfYf+Dr6PGrMtqZwVZjCVI2RMLRkCtEPB4k+rYRzKRnWhGyr65XZj0AsGaVNpQBWImrkuIijUngygsaiicwVTCvE7CbDxyjA8OmI8m0DlhjEHLCikKNve20bnj6PCIn1cF29c2uTebUXmfU6SgU5GFSDg/YfFf/kNWnz9g/Fd+h+kPv4s92MUU5dr+jDE5fKxNqW0DRUq6PLhOocq5xzGm9JfZxYKTp4ec/f3fQ/7gn6LzQ1RCn03lnh7zP/kbf43z55HPHt9n+4N77L/3YXJAkO9V8phkI8ZQIOrz+krpbon0LCkF0QdSeYeUUjAev2mkDamfIeBsqvRhEJw11HWNAmVRrMvzFY6yKGgWh7BxnSglyd8WWawaNsYl9dznXG3Lk513MG3ge88/YcPXPc9JUuAVDZGLzz/lF//+v8/u97/Pjb/xP2LrG1+HKkH2jeRoYT6EjU3r3QioGjTEnL6YuHHUJ4iosUXPbxPbltWDhxz+g/+Ko9/7J/jlOZaYOTWSch2zE6hFWYmwKC2bP7Tc/XaLsfkEzfqpibFfT72hr53RnBZ9p6Smd4fOG1hrf2+4dY6IYfTmEqpnbbjL4Fmv9chXuSQGDjIM5wvP859/xg//l9/n/PYt2p9+xu7zJ0yPjigXK2wMiEZELygWc/TBU+LDn2CKTWS0CaNNTDlCywpMgRGLxIg2NbFe4eoFrM4x7RwTlgg1SJsik6oElFhCu1uyuL3L4s4tuHWXcvoO6CaJkH0wBn3fB1bMwKjv2tqv1Wk4MpjX7vKhqv8WFFQdmnJZybtk5naunPU1qoN7GXAkSF7fUZOs7jmzeiXXZOdKxFqLiiEYeN7WHC1OuDZqufu1Dfbf2aU8aYjPXmLOTijaJYYmpeCQqud1FRhjN46SzvKgglLgpaAtR7QbU9zNa4SDHU5HcGhg4SA5x12S/0qSSfl7L++W5PzsnfAwQOFcWtH56ivD24/b1VfeVOvM4Vd+qP+p1/5iZ9zK+v0uTLNmZO+QGmsDcu32cBwvWxZbE5bVlCie3dsHjB89pzo9wi4usKFNTp8cRCqCIssGli3KRSJv7+1VWXNNSXbMaDK4O8q/xM9WEMoxfm+P1Z0bzG9e53QypkHZ0UDZnbvd/90yHRgmw23anw2yHsduVjWHr7UbE7TXNd5Oy78+NF4vGdP5/jXT22eHxnD/DvdqJ47NEPFGdz4YOsSNMxmnHIVVG/nZySmPS8vB3Xtcu3mDzeMzxienVPMZpl5i2pB4DFX7lEIhAdy6G+1ScqMKXpSAoNYRy4J2NMZvbVLv7zPb2eZiNKIuq+zcyTqnkgNk5F8YOmuk7wdy2bUtkDkju5TmK0gr1j6StzWLXbvkoNDhutGrF/brtfvnVVnfeZoElZKweY2z2x8yOwu8v/2C7ScvGc0bLJJkZXOEa8+wF48Jjz8m/Mk1wsEtzI2byLVd2J4gkxItbOIisnkso4KP6KpF5yvi+Yz44hR9+gx79JhyfkTh56l6HDHbI2mfhVFJc2OP5YcfUL//TcJ0NyO6pR+Dq/2GgYOjHwRemZzOudCdSW8bZXP5JrodOHQHD3Ss/K/0z7Ls4LI6lJcyvirh/XtoYYlFgf7i54wXSUcExbYN3P+C9uSU+Pl95MPvYL/5TeT6NdpuG8jgtgbOejStuy5rQ3TtVO0O4m4sX1FVAFSIF5H250fIj7/AffZzqrMvqcIxhiWaNWoVZTH2+N94j9UPfpPlxrt4SeXCXx2T12suwpU18QaPxtGoYmO6SVSLs5a2qQk+IKr4oNSNgWKTrb0K78YUm9u0bcyExCFJsAjGCqYoiFIwWyjGRoxJ8tOKoI1y3npOvvUuLw5n7J0vGbfJHta4Rt1Dsj8kBCZERl8+ZvXlY5aTMf7GHty6xnx/j9XDIx7cf0ooLSqCb1MBpfahY7yxiW5tY2/coJyOWIXIatUS6obVbEZoaigs9WKBPJtgLpbE8wuCb5neusakLDj51k0+P3rAP/iTjzgNNXffuc72dEJZOu5d36YoUvDZ+/S7YdVwfjbn6HzGZH+HaWXZu7bBLpbCulyAJ02cRXn6xeOvnJNfDWkjJLJdMViJlCLUdAs3wcfmPpGvTpxh2SaJbq2FasLIjHEKPtN/mkIoxVGMSnzjOZ/P+GxjyuSdA24+OoRVSzaJiRoxWLyPLH7+Oc/+D/976n/lX2HzX/nXcLtbkI1CyeX0yJ4uYsxJKpnnhC6FRpIXmIy+SR/KnMaJzDSRBinBRy7uf8mTv/t3OP39P0BXy+TkuHbAfFShROLmNhfX9gjbE2789l+iHE2BRGwqmfROSdBXLRIHjBohmOQwMprzJE0ax07hN1058DfURCDxv3bqQFJYHJ2NnWCBQZOBfVGnikClE04WAR8Co0nB9PZN5lZRWad1iCtSnj2Gg7u3mB2dcv/ZS9rtTX7z2jZff3FGoZpZtGNW+CWRJGqLPnvCxd/6j5j9g3+A+/AbbPzl36L42nvM2kCoGyYjx2QyAdc52DQrX9n4GEjctvEsj2YcfvwFR7MjPvniPu1nP+WgmrHtwTWayzlD/fCQo//g77DaHLP529/mxm/8CFtMUF07a6BTUPLakRTvCiErqqbPZAWRRJwMPY/S+A2nR3UOEYHEG+NSqp8GKApH6z0x+oTmApy1SUj6OaY5Q8f7iVBchboJTMdCZaDu0E1ieHTwLsEI3332CdOm7s2wDlivqmi95PgPf5+Ln37E9re+xc6PfsT2hx8yvnYNKUq6VAbp7jmmVDI1OeYaA8H7jCYTom+Jsxmzj37Gye/9HvOPfkKYn+F1kBs8+BvzWr2whmfjEbd/TbnzvQZTpH0lmlOjMvkyQo500kf7+/mN2dAZKLDJ7QY9QdZbaZfztpNe3APyL+fka3/FlQM8Xbv2X3Qmk6yJIiUSwgpvPWd77/LZduDaaJNb10/ZO3lB+fwF9qLGKVhRRGNSKutzqA16ZjBZne0TKSUhzDKYOKNp0p3EbKFGUdpCaXZLlrf2mF27xmzrJqHcZjNOcFr0+2poaJEfXiY85YpJJZcuTsrdWjnsP9MZtVdef1Ptkjolvbm6vuPstOpTbRgqW9pf3SlpBklGIjmNkLV+2TlwRFLqZdBICAGPcLxSjtuaL/ScTV1xZ3PM7R9cZyt+jfZ4hRyfYGczbF1jgqcjT09rPu2M6EriZEzcmBAOtvB7UxaV5bhdcdIuqRtBnaV0BaWsZWTUzsC6hFVYq+qdHOgV3m4fD/fWoEZLnipzacrWCvMbb7JWjnsFvts7WfYn43bdr+G9ds7mdd/W62Co169/TnDGAZZW4aRpeHR6zL7z3PrwHXZnN9k8PsMdHSPnp7h2idPQQ/O7wIrtyhSSHGMRMjqKjG6FiEnEx2UBm1vo3h7z7S3m+7ssphMWxtCGmFJ7r9x/t/J6eUonabIj6pLs6f4d9jSiPQrn8ji8DZMxyfXLKWnr37li+Q2i2VzqwVCGdCdB93KX+rz+diFViURzJVOgbluW9ZxDI3waA9vjkutbd9lrA1v1ismqplitsIsFpq4Rnyqw9HtEEtFldAlNEyYlYVRRj8asxhOWW1MWZcVMoclz4zRVQkzoD8nrVjFXZvTqqERdnzUdqfzl69b6VX+W91vjSirTG2ivQ3/8Wb+xXlNXPievOy/TKyqOk5Xy45OW6pvfwdw5ZvXpF5TPT6jqkNLaNGLwaZ4Wz7GHvyB+NiVWm1BtwDiRSasrSNDhmKrpNC26XMByjtRzaGbgFxhterRVygXIupAxtLsbLN65zfm9d1nuXWc82sGK69dE19P+dB/InNf2sYvAXvlk93XD4/at7MWBRyFvy+wAflV7WbdXpeWreznpcn5UEd+5QxxVhMmI5k//mK3FReIFAyRGypMjwsUF8Yv7tH/wR8QPvoH77ncZvXePthSCrlHXw/vQ7oc7eOglSX75vtMxIYhYmEP90SHxj39B9eATytOHuHiCJaGpuhIfUZR6C/xf+Q6L7/w2dXmbSDlw2AzH4cqZ+nbUmFfadDrid377OwTvaRuPD12AIKW5th6a1mfkTKD1nsZH2tbT+JYQEkWKoETvE9enzTxsUbPSIDlTIjAr4ek37rJxVuOen7A3m1OGTCREt7e78zkJoJFCeX5GPD/Hf3IffMtzLyzbQFMZ1BV4gbr1eB9QY/HGUe7ssLm/h/ceV5bgPc3ZnNDUWCPYuuHCe4pZTekDGOX42i7uB9/icAT/9PP7/OzhS5YEVhjeu3XAZDxmVGmqOEUKYCX2m1QVebVqubhY4OKIwq4r60KiMHO2wF8s+KPf//gr5+RX5LTJ4l+TkHcoZUjeWg8QHQuvFMZQlcJJC22MWKds7l9jfLJIcHoreAcLcTTliFFVsLGxw/z4nIfHh0zu3qDa2uSgOaEjLhTSgQSKj1C/PObo//WfcP7zj9n/63+Dyfe+i9vcToRT+fpUCjwkWCeJO0NV+9LSPdy8I0rViM1qpMZI2zT4izmnf/IRJ3/vv+Ds4afUIWARrBh2i5Lf/d2/Cm7K4Rdfcj4e8d4Pf4uNg+uZ+C1Ft0w/fklgGVsgHd8JKc0gIX6SgU8bEozVGKxJXrg31XpFNP+eiaZXTFNaSop2qyTw3qxJqUwTJ5wtWqL3VEXFzt4Oz89PUEhGONmRkome3WjE9a+9x+PPH/Dl80Ps9hbGGe5GGKniouIw2TBRtPVwdpIOsfNDmof3ufijP6b43/1N/vbv3ef45X3eeec6/+a/8T/mYP8m4jQ7wjJKynvCsqU+nTN/+pL5gycsPr1P/exLwr1NgoXP5sec+sA3C8NmMNiYKno0BFbtgs1vvMvBD39IMdkcWPIDczgbzyoQiKgGxFlQjxjXe0pFBB8SKbbJa21j/IbToxSqsiAEQ4ypOpcxgvGeVV0jCtVojJnNqZtzYgw4awghEOaHSLXDUOrXdc3mqKCee2LGgUXjeLT/Lt44Pnj6MQftgiJtlBz5TQInaESXFxz+iz/k9Cc/ZrS1w9Y3v8n2D77P9L33KHd3cNUok55CNAIxEpoWXzfE5YJwck796DFHH/+U5tPPaI4PwbeI5Bz+DIvyAkY6h1ua/cbAYqNg+qHh3vdqiszZlIzRJAu6fG+JIEb7NZ5V1TSk2dmQk5PT9RkSfsmYfMMTuVZIvuoUXl/TkfPKQBG9ZC6t/7n6Delaa4nRUBvHs1nLcdPypJiye/MDbt95l2tnL3APHlKcLSmiwSFYUmrbWrsbGD5qMvFz1m80GYpRAkEiYWJor28wu7HH+fYei8kevtgkaIULHWKkc0asnXFD46ojFu3eXxtSr6qbkbXi1f1NS2Woyr8Vkz//6FqZSPcgvZHTlZVnON8iaxh2P8fpHlOA4VVlsntsIEVpVFJKrTGIswRvOF0pD85qPn52zk71nBubY27ubrH77W2mxR5VdBRREE/ijYueqIFYWtrKUltlGQIXzYqL+ohVbQliETEYKxS2wNoSaws6o7anMLnazf55p27nBBkxCWGgOhi3y/N2yXTOjre3shNlsIr6Pry6H7vIr8rle+3vMz/o1nBPpCl9Rme6Pj9OZ3vSU7zCWeM5a2Y8ODtngnJzp+LmtTschHtMzlc0h0dweoara1wMiZeLtHZUtE8+Ckj6ax2+KGBnC3a2WW1tMZuOmVWWubW0IpiY1k6/CXN1sDVibW38p15pr0NkMZnfid3dMDQo03KOQ9vx0hy/jXZpdnqL90qqZV5ZMvjEq6uv+/g6pfaKZFmfJcas5xvNnA6Gumk4vDjjYdNQFY7N8Zid8YTN7U229nfYQpiKZWJSlDXxCUaighehFlgJ1M7gXUGwBUtRVurxbY2oxVjHSCRz4nT6WKfDrKmEJb+ej89ev+l6+gqKo0PYDMYuM01cksRvYyK7ffOKMBnc2/CGL50IA8eAXnIaXFkBmoqWnK1WnI8PmN19l9V0l+mLJ2w9eszoxTllq9hApjIISGwxYY6pX4LYjAJIgYzkKFO6tNOOuVY1EiUFklNAg/6eooF2o2R19zoXt+5wtnPAwk1xaqkU3EAXvTJC+Vxb93zQUzrN4HVnx+t0jbd3KnYSe23HaK+bSS8vL9/bf8e7EYhVSbx9g7C1QTjYY/Ff/yMOLo7psEmi4NoGTg8pzk8JX35O+/u/x+LWO5jv/xrVr30X2dsmisfHkOUbvezqCpqkGx04yEQw4jBqYRVpnl6w/PEj+Ohzxi+/ZNQ8weoFQntJq4mitDZQ3xgR/+r3mL//Q1pzjWSOr63FyzJyMF9foeO9jaYRQpMQ/bEOxOhxhcFIykgp1CQ5VxYgZVrnGnvC3ZSGFhNHUPCsVjW199R1S9RIiJEQ0tkYQuDw2SHtXkBv3CDsTmkfH3Ht6JyqaTAxXDpjOySragCNGFGcwJTI9dgwDS3LWUsdIk2MrEKgiRGvqcJzeAYLtThjCCKU1rJrDaUxVNYydpZR4ag2Clw5YrW3zYvbezzbNZw2NTt3r/OjG7uEHLTfnFRsTUYUhcUaQ/BpDDpU8+Z+xa1ywqgqKcoKh+QMIOnXW90EZmc1Ov3qasO/YvWoHJHL0LIQJaM20hd5EeYhRV5GzrJooG09rijYPNijvfBYEQqEFgjWEKoCb4XR1ibTjU1ePHnMx4+fo9Ntvrc5ZXu+wMVALt6VRKNGMDZN9mf3Ofzs/4K7d5fRb/020x/+OsX1A1qNBN9iRChcgXO59FsP1aWHZyvJCAwIrUba5ZLl0QlPnjzkxeefU//hv8C9eInLx3aKKkbOHj9j+o//OctRxYsY2fvw2xzcu5eqC0FPYqymE/gpX9lJ4ovR6NdpN5oIyBIyIJnNogbBvHLQ/EWb5pSxThEOnbHaGWYDR8W8Tc6HqYPFKuJ9pBgZNscT9OKUIDk6k5Ev0qUuiWJLx+2vv8tLlEfPXxLKCr+1xa1ly2a0xLahIMGD03noUQ2oNQQNNMcvmX/yCU+++IKLs2ecvrjPO8Zy97GnmBRo4ZJHfV6j52cwm9HMF6yaC1ptCdHTEAjLir3bNxiXJcerc2ZiGUkkitKoUjth8qMPOfjd36XY2EPE9RtJSfmrZq3DppS3cgSS0gxUA8aOkegzhD1FvUL0iE2cOOPp+M3OYT6QjbWIJM+2sQ4nwgjBmkRovTEZ09YTLmKgrnPq4OIU3VqB20zrUpW6bplOy5QWGMhEvEkpeLl9h/NixDsvPuG9i2PGGnoItsl7KEqqHkdoWZ6+oP69Fxz/839GMZpQbmzhNjew4wluNEpl731LWNU052fE01PiYg6+pSWkcn+d3dvJs+EhivTGeTDQXnPc/JGyf29B1Tl0UNQkA8Zq9zgBZpK7NNJFIFUhGsUpGQY+OJw1KeFGTKp29IZn8bXtUmrUV3wyp0pcTicaKkWDr0qPkpJhHG2IhELwGojtivmy4fNF4E/EcfvgLl//nW9xQ0+pHnyBefyc8qJm5FMJ904ZVs0jmB2fQVIEyVshTAR/MKa5uct8/wandpelLzEYREtsLOlwTMlez0bea5W3ZCwNx+MVwyL3eOgU6A39Vwf3zxzbP18bAL2lMxbS711NO+iBQ935kC2o9d3TewBUB8rbwCPSydzSOhSLDyT+MyQ5ziWlTNYx8mwVeb5a8dHLBYV5ijNKKZENJ0wmBVXletJ2L4Z5IzTqqEYTRtNNimqEsQ4Rg3MWVxSU5RjnCqy1GGMJwedzrzs51iN/maSZwaLs5mnNI7JGeXT/aD+WKc0n7dU33dYOG720d3qlvb+mM1ilE0r9e3agTF9WuNPClk73HxpYAmKTYisklHIQYdEEnp6d8Llv2ZyU7E8m3JhucOs7d7gx+iYbwcKqxS9WhPkSbVui+iS/CgeTijgd48uSZWE4D5GL2LIQYYVS1zVihWo0oTImV/mDlJwTsqZj+jGRrs89MnbYz+EazTXB+qHpOJPyGPR75M8/V39mu7TF1wfJ+h6765JxfdWV033J2pkzNIgHATTt9KYU7MLalBYcM0eGsCavF8HHSLtcMV8uec4pYtJ+MsbgFAprKayhNFA4IUbwCGoMtigoXcmoHFNUilhBbTqXrHO4IvPYOYe1aT+ntPr1XulcN2v3lPZOt76nw4XbKTz59asOybdtNvaltr9C//0LS3IRjAFnwCJ4NSzshPthRJze4sZ3b3D7g3M2Hj/APT6kWCkWm6ro0cmAkPkO82mTl0say8t3lxzOaWd5lGAgbI+J925yduM6J5Md6nIDLw7VpBdfIuq+LFT6L70yVfSTOrhuPedXgiKDffE22mUJud57fQyoz1Fi/cbwwsH+e1VorF2H0RacFxO+MNv82r/5b3H4B/8tk89+wSSxsdHpF4QWG1tss6Q4f0nz6U+Z/5199OY7mK+9T/HeHaprB5iNKbF0iQcxQz2tpOyMULfE84b2aMby0TH+y+fw9CnF6XM2myNsPMeyJGFrc83UHJSIorRTQ/3OPuG3vsfq5ocE2QPca2TWJa2Hbn1lN/F6ioVL0upNNjFgCwPGJdRMsIhN57zJPez896ppfLqzGk3bI1WfSg4xH2N6rkqI4GNKs1JNTh1Qzo6P+UVTs7x1ndnXb3Bvd4ObT47YOJtT5syBy+sCekd0flxZiylhFAWvER9j+n3tkG0gRjAkB0thDKU1OBGcsblalIOiIGxMuLixx/PruzzfKFjayGhU8P72rbWjXtLOcmIw2YmDkjh78rL1qtzOvoFCsv4bfNLXMngjxIi/dZ3bH77LP/34o9fOya9IRKyotogKNgIYGhGsgAupxLZvA16FiRXOZy2+TRG47a1NgjvuD0qwlCEpOkENtQj7B9tcKy2PP3vAj1fHFFtbvL8y7BtwQTPkTSjEogo+tNhZoIme+idnzH72C47+0X/Fzr/zN/nZ+ZJnX37B1uY23/ut73Pz+s1cZSqrXom/ioS8UtqmYTm7YHl4xMknn3D06ce016/xk4cPOT074aAUbmnBRp74IOBDZPbZA8LBLrv/0m+z/81vYYqKztkuURET8wZLiJCApfUrKjMlRJ+IszI0LJUF7w5XzR483mx6lIKN6UDp+HZUlWjS43WloITEWfnEYl8WhvNzIeZ5nlZVKp9muuM7O2y8R4rEtm0NFFXBu9/6gJejiucPn/KPZ2fcm0z5mrfcaIVxiFSA05jUIWtSelhOMVp88SUaZxhtsAGa0yPmP/4Fzq9AU0641YiRQDRCrcIFytwKrUssy+HsgiZ6orPUhSNsTWhfzllGzxGBzR98g/3f/evYaXLYdILHZui/ywZSFy00IjhXgFrEZmF7tWS5Kj2nksBoXL6xOYTuJ7ITI3v+QkxrzLnEMeE1UK5GWGsTh1RR0rYtzWqFzF9itsZEcSDQAstVZKsynCwVr6T0mPz9i+k+n939Ie3jn/D+2VM2ENCYIoO9EpOYkaJmorbokcU5cXFBeCk4zSD57DRM1GKaD9U4UBqTsyXmdW8zqq8T0ZG0dn3laO4Y9n8zsrOb0zz6byKfc9qbkEmxEUSSs0ZkDfKXjMDroqamg4nn+YsyNE3eboudoT5Q+rqg4rpixMC47UdGrwYf+4OsT2QQICjSkjz9RmgI1H7GxXLJ6fkLfv5oysbWLjdv/IDb35iwJ0s2mjOKiyOYXxCXS9RnmGxhiFWBliPiaMpyssGqrJhRsVgKoQZCxNmGqiooOtsw5zMPFbF1PZvBvwId183QuLiswQ7mV7txWKunlzE2b8fUWBsWsr5PpUd69Q4oHc5JSvntPkfXB9Zf0H2yOzVVUupUX5HNpsIAqh5LQsJEI6g1mNJlsv3MKZSjTDFEovdclHBjWzhfLTk/axhXjhs3NimnY5aLEq0mmGKMNSVisnJTpABI4RzWSqr+Zk0y1nuHJ8NpZQ0qX8+hlSQ/XjcbnS8k/bm857T/5y20q2kGV58P0DXCGpmS0r6G+Cp6mQOXDWXVgeEk6ZzsNrPV9DwYk1F/hjoqq4sVL8+X/NwcU1hLKTCxwvaoZHs6YbxZUhQlSEWMkTZEVn5GfXEB1mFHFdaW/X4TY8BYjLUUrkgExdb0TsY+VbEzsCQ575Niuk7b6/4ZjlLHvZPeyYiPLEN71MYlAfUWJnPgAB28yJpTp1uiHdnjZVnSjUH/StfZfilK7uN6xo0komcV8DEmh4nLByuSzl9XEGNIgRNNaJpFTjnQpsWJ51v3dtndmXB2vmJrXICxPD1Zslq16MRQWME50zscrElzWJSpMou1Fuds4vPL1nznGDb5nH7tmGc5Fbu1abp5WkvPLilsfS7KwIX138/ZCOsz8HUpVJJf/6pVNfxMVjVwxiQjKiRjcrFacXJyxnNb8Olom+vf/hG3P1yx9ewh1YMnFCczXAul2FQspJPQqr3MJ+vxHRegauLdayXipwXh5i7tO3c427zOobesVDBaUGjn2IWeT+hqP6XrZ+4AAzl5CVXU9/I1AzEYsLfY1jQMV1qkL/4wvIU1xcBXi4lhIKpzJyOGZYSfvzjlweOf8b/+N/8Si7s3WH30Myanp5Rti+syKbJuLjFQtgHX1MTlIfrop8R/MmFZTIjFhFiM0LJEXQniMFES9+JqBasFpllQhSUTv0TCEhMaTPSIrPlqokSCKtEI7chQH2xQv3eT1TvvETduUMoUxPY6z1CWrmf21Xf7gejMjeHYvPE5TaioVNQzo/k0oWMMJD1d0ph29iRqMBgCHo2ZoiBqoiuJihWLGsEaQW3ICLR87oXI7OyCz1YrTq/tcnywx+nWbW4+u2D/xWlKK40h8SmSUegd95EqVgxqi+wMNyiRGJNevQ7oGcSkLBEjySFnsq0txiaC/vGUxY09Dq9v8nx7xKywqBOspgqZRlLZc8nFgox2qFpNwW8SkCGhydO6K8qSrvZKcvjaHAA1iEnfXVUlU746yP8rIm2EKqZ8NBdySeqOyyPDbxoP0StlKSxDwDchsUaPRzhJkYMGQyUpWt56n4zumAzirf1dwPDs80f85PyUelTxdTvmYF5ThkCp2djICqC2gajJcdQ0DcsvH8BPf8pPnh7x2c8+xhvD/OwFv/3OB5TlBDseYcQgGgmrFXq+oDk5YfbyiIvTQ5qzU5bnM1Zhjjs5ZmO6yX0ii7ZhgjAWg0QlKDTWEHa2uPYbv8G1D7+PjMdEa9ZCtVPyMqrDqsGKSWRlNqISiRpwzuFzpNOKoCFBvowC5tWD6S/ajCZDXFMmEyamsul0ULZ00yiwCNC2kUlleBlSKWsRmBQuozFiT2Iclqvkya4SaVWZU9CktBy8d5tqe8rzzx7w4+MTHpclX68qvl57dn1gCrhsRAcUj01jcnTEB1sbPDkNXC9GbDUtrTSINBhJnltICkojlqOy4P7YcWggBuV2NaFcLDksHUd1g4gQ9nc5PGs5Ci2PC8u3d3Yx1SYaLTE7ahLxWBIAKMnIlERc2CEW1AuYRJwrxoLJfnXRzKeUeiLAqKq+cj7+vK3jgojdnBmIPqWymKzMTTemXJxXLJdLXJGqSRUu4Gcv0Y3bSTghBFXmq5pbOxPOVxHfJc12SoCkNKQvxHNk4WtBOUCpRCh17fzwnaGcD5JgtNNn8jXaz1eCCIfEDqCaVWiL0eTBD5r2QyQ5kTodpbWG0+2K0dcNd75TszFOjsTkCMr3LTndUEkySjL0nA6tJ0jMKA6TFXpRbG9Yr3OINcp6H74Vg3GoQemll1/d+p3Ceslr0z94RR/PirfJB4l1tk/jG/gGsjNSibGhrRuOX5xy/OIBP6HE25KynLK1sc325gGjXYexgI+0MTndJYCbC9Ui4hxY47Mhk6utWYtxBusSlF8HXY7Z+Tfsx+vbWtkerqX+1bzI1LDm9Ol02YE5/fbbwJBVLhOld4Zu5k1KVQm7CyWblp1iIZf7Ktob/J1CZ3M03ceQnLfGYJxNe70oMNb2Rrb3qQqiasRq4O41w7Rs+OiTOYcnnsoZpk75xocVzyYlrU4ZjTYobZHuS4SyzOianFLczV/U9Yz0PFCsHS9d0nIiP9c+PWhoLkMyCuPA8NAsSC6VUNdftkb+/G29Mtb7MBk/AydZviJmaSXIWneWrGIPtleXpt0bF92gSY7A2oSKBHIkPjmzEyTXYmNJV7kyxsiyDSxi5CgE7h/PKOwRW1PD9s6E0hoaH1l4xceCUTliazJhUlZodrBZ67DGYl1BWSZnj3MF4gwxxmRISd6NHcePSi/70vnGYGV245U5VFi7MtL61gwPz2dC7+h6gxP32pbuSaSvt0IvKyT3o1+B3cxeWmUMEX6dw7/fgflj6/SOVAmxIytOa1aTku8cxjqKMgduVBMHlQZsVJwJiBG+fXebe7e3+c//m084vIBrOyN+69du8LV3dnhxCrbYxBUVifhb03dWVUbb2Jzyn39fpEfjrTfZZQRcN0p0hlZ2dPfyJTtlhlW2utSRLkyz3iVvo63PtNeqwH9OvXjtBMnzaJLc9MEjGhACKoFV3XB8eson91vG4w1uXn+Xe3/pQ26yYHr2nOLoCHN6jlvUFF6TUR+1lwXBCb6whFGJbmyge7s0u9vMRpuchYKLOlCfeQItZVkyKkrA9s6WGPW1jqnXDvdVB3N/XYfEWX/HFS3jv9c2VKHWMmAdrumROJ1HDXq58vq2XoERQ6NwcXKE3y1Zfvd7LKttJo8fsHH4gursjHK1xCVaFTprw2hE2gbaOcoZNoWCwRjUWLoKwAlVHxNPZPTJ6JaM25BOT+7O9uSwCwaasWO1PWZ1c4/ljRvMtw4Issk0OMrc72Ff1l2T4Z/++7tl+7pJfOPzqplrVdM8dBxqioKJXa2VLOu7j3RBCQuEPB5rZ0kSq4nPJVWITpUtu3MwpdhHfAi8eHbIxfmMo+sHvHNvi+v7U/ZPL9g9nTO5qCkaj2ibsm+ys94YwUnSI9I3mjUPJ3mqJGVAmCz3k4FgiEVBOx6x3Nvm7GCbw90pJwWsTKruZCUrmEBXEdIQu3AIYjq5S9aNJCHJRZKYEZMRW5rtCjPQhXMgUwbOy9e0X9Fpk2CEnd6hKriYjNtghSCwaBPEZ1wIi9bQhhbRyLSocMYlSFJMqBmrERNajEJpkmFhrbB/Yw9XFrz8/AEfLVacbRa8Pym4vYKNuqFCkkcaAUJSICOJmyYKR7/4BWdtxBMIwXPy6Alf/vM/pTidgSojASsBbQOuDbQYFigXVmmdIWDw2tA8eozb3aWysDRKbQzeCyYorRVWB9sc/M5fYvfb38GOR/0hIpryVEXyJpPM+5DCHTg7xpgyH64ObKfoJqdJjClFxGBZ41LfVJO8OCBFsNPC1UzcnJZNgrl5VeoItY+MRrAIwqL2bKCMCsHmjWytw0aDPznFaqAaT3PlorSIY1Bcadk42KKcfIOjL5/z8ulLTtqax67ga1Z4B2E7kqpWSEqf8wjNkyO2Ty6wxrDvofmTT7AhVTnq0gGjJL6iiIBzbBzs8qKuOa8bIpGyLDiqW7SwFFF4+PyMC99yVjhWIiybjjPDZNmrvYGvUTPl0NqYQg0xcxEQwZgiS9HkpFIhGcbBg03vlVXxBucw3W7Cq2kSeEgi+eqkkiRIdVWNGY3G2HKG9Y6iKPGtJ7QrzOoMN6mSSaUR3wYuVp5pYfB1JIpJBpnEJJhOHxFWpxwKnBrlGsK7KlxXpaIzWNZjF1VwMacTGCXETtVPlTD6yKeuo9GtKFZzlEpypEqk1zTmlaW+U3Lju4GbNxY42ynlktaErrP3e3iyJoPC5vsQGZbT682pdF02cDtXZDLYkuDtypy+vabd1H5164aii173anc+EAcm/fojOvjfEmOqV6H5sHFGKJyFsuirtIUQgAZDAz4Ql08pl7DhCy5OC+rWsV9Zxk44WimYgo1xRTEZYcsxGIfJMNPCWlxZ4soKYxN8Px1UEU0n2SWDeDgevfHYOTYGvZPBv71LRvp/Bu8pl8fpzbdXnGWXrb/Ld57fM9mS7WZdgK5yVO/0EFk70vWyqawCxiVOhRgipkqoFxsNzhjKsuyTP1QV61z+HWWn8tzZC3zy6SFPjxrqYFj6yCeP59w8KHnn3gaHrcOVYwpbEELKa7fWpsICg45EXUeU+1fz2aBCjhL0Q5F5z4ZO0OEIvJrk183Z2pjsze831vS1z6T3s8TO2ZDf6ZxLa9E0dESsDX0Grylr91X+9nT2AiF4lJyGaw1WXUIzdc4SzaTtMaIx4mKgksgHd0ZMJ8InX5zw+Kzl/ZtT3n93l6OlZb4qcdUEWyakDSKItZiMlHKF6zlQjE1qp0nhxowIWt+5kHVV7aLba9fcWgwNDQrJBs2r3AxD185bQS+mBZ++//WWbL6LSxiowXx116xTT9bStePtISOQ0vUGwWIQY2h9KqtqSSWa1ZGQuTm6a0Voc6RYVJHo2R8rH36wx9//Jx/z/DSiYjk6a/jsy2N+9N0Rtw6mLMKEaryBzQ42VXBFgeuqj+TORtWe5y4vnn6OXnVe68B6Xpe0jypDU3o9pOsBHozr8DveXLuKGr383uV1s0ad9Kf5+h4vf3JwfXpupMCISTIuy1RnTa6S0xLrC84X51ycvOCLYkQ13mAy3WJn5yabtx3TQhkRKAkUJH4Nr9CIoxZHg6H1ivcRXUZYtiSek4TIctZQlAbnTAJsCwwTRTs99+oYX3W+DGXka3dVfkO7wR1+/m0g3ljvpO4nh/eml94d3s0Qm8mlz/Ytd1YuXZNsER8VT2A22eLLcpPptVvsTadsXZyxOTtnNJshF+eYpqHI6BGyPSmifdlwgqRs0f6mJQfas2zJOmxH+N71SVFCIcStCcvNMYudLRZ7O6y2dqhHWzSmwnphEsOgU90p8ppVq6++NhRrl0/JN9wkOaLDwIHUByeMAZsc/RozCX5Om+8oMtKH0iglZ7fN58ia2F06Sgak1yOiakLSKMwv5ny5rDmcHrOzscH1vTG3b9xkbxHYPFsxOZtTLWqKVYPzLSZERGJKVc2SXvv1njuAJIeJtUTraMcl9XTMcmfKxe6U02nJrDSssj1nTeeISdjozi3XIUs7hJ0xttc3O0eXyQ50I4qRRO3SFdeImmxOsx7uKw9ebb+y0yaY9AtLEYwGilxtJqpJ/qyQHBvjQohRaEMypidVgfS8ElkpiA1Sr3DWQFbuC1Lpq+3NKeU3P+D5g8fcPzrj2BqOihF3EfZbz1YbKDVirOaS313Ez+CPT9ja3+WljYww7JQVlC550tuWWn2CymkiUG6LgnlheGZhRopgbEaB2HLeLGkyB0R5/SbN/WcsYs0LB1t373DwjW9iqxEJXRESj40k1Ajdkokk5chEojGJiFhGdFWtpKv80ykEmhETRf6GN4y0CbpWnEze9gnClY3U7IgzkskMgzIuE7/NvEn3uTUuMCheLFEsxEhz8pJJoUy2RrjKJbNXACdITGku1chy42vvsHF9h5ePnvLFy1OeBOV6VfJuWfKeK9hqA7SRaGwywpuWsrJYDah6gol4TfngognbERS8CtJ4ts5X3Hv3Fl+ennF8ckrrPXXwECUhr2YzWgteDGggNgnCp6qEXKnFE5AYUBWCT3mZjfe0iyUxNJydPkGbM9ROcdNNPrhX5OoR2fAa2M5iBFfY10/Gn7NpwlcjziGSOWa0m0mTjXFHdCWbG1ssl8uUX+oD3rf4EAnzl+hkFxGX5loti2XD9uYE27apmpJJa9TVJ+jF4+S5No4okWcaOYyBPSMcCFxXZRKhUKiyAhjyOrLR9AdaF0XpDp46n0oGaDUZLU3h0pxaodnapDVgigv2vx75+r0VVRkwko0mkb7yk0GTYwZ641fyuEST0CTrqLlkFEQueT/QEJJYT/drTFJb4xveh2sF82oaxuDx4NHQSSPdgShrheG1v9ArvooxinEW731WGGNCv8SEjKMs6VT1EAKJ4E3xNrJZKt+8bTgpHZ9/sWRxHllsWn70rR22jHB0oVSFxY0SogZjU25wjjIbV2BMmYnVbU/KaiQ7pnMU+Kq6/Yr6femFq/1ex8Jf997bcdfkb79k5V4xT3WooKZXbT6mk1OSS04pybxrPanpFY1NBn+NS8hNj8eY5BBJkWmhcC6Jofw9XYpuaYU7+wXBH/PZ4wtWMd1LUDhdwUefzfhre2dc291iKSDGIeJz/CCfR93frKxIjhJpx08kJOcfgzU96MoauTFwmnZGdmcIpgGiQ312gykA9vUm2V+kXTX5kjKf5tUmbPhgp165r/4TVydL+vuG5PwZXiOS5IsP6dedCFhH6yO2KLCu6CN3PlfiUE1Omzs7wrVd4Q9/+oRPH69QhFUdGBXCnXdvcDKdomaDcjTp01qMMZSFw+V92Hsq8lwkWHd2Jgy2i/bXrO27ToZePuwGg5l1lyEvU/rQ2sCJg994W20tV7qgS75RWeNKTH79qiGZ1ulwxtbOqoTOjP24pFRBSxt89+H0yyJI4XDO9EvbqetJpMfG8+17I+4/esbnj2cELUBTNbjHLxbcvn7Ou+9OsWqQMslQHzzRJyegHRSqWO8fBtOSEMCqHT/ier6GyW2p3+u+djxECpdeTXs7n6Gd2HvTm1HWe+9S3+jk4tWt1d3Aq3J++LmuDzr4T4wg1qTiEpCtyORONVaxudqqak1b1zSrcy5ODc/E5LQPoXSWvUnBpDLJ4DRdBTHw4ihHJRvTitGoSrxDGJxJDvCiTOlttihwtsBkxLbJKEqR1/Xpshzq+i7raXrtnGR3w+CiK2v9LbYONfQ6p8SlGxkcFN3ZoYML+/2cz5BOZhtJvFBiLKH11Co8nze4JnBSVmzt7bO1scHmYs50vsV0taBcLGCxwrQ+F13IWQevaF9Zh8yFcdK5mrlZFIII0Qk6LgmTEavpmNXOFrPNTVbTDdrpBsGNiTiIYE2X1bDWh18ZmORtYNj7V++qW81vR8MRBJzB+ASoIKckIQmtTuZ0SYCEdBcSc8YBHS3+OiAlRjEuOTKiV8g6Zoc+DJ1+HiKE5NQUJ/jgObuYMZ8vOT4tebY5ZW97g53bU3Zub7JZK5N5y2i+oFy1FL6hbDym9X1mCcYirkg8Na6gLQvqyrF0hsXWiPm4YOFgKUqLpkp+tisZRNKZczJkh4nunDNdi0Hz0aI5aJwLxMj61Fi3HLgyWf/T9aL/ZTr9r1g9CmoDZVAqTdMSjMVjU3Q8c6A0ISkOq6C0PkOhC0stgaAu54AJZjVDFmeMqgTlLYzFSZFgTRJgMuLue+9wNjni6Nlz/mQ+48ui4IPS8DXgIAQqTYiPYHKaiEI4PuWaNZxh2Bah+uIz4nKFhAYjQoMQ1eAkwZUWIhxOK56OLKfeEyLcLsYUjeWZRhbR46xlpsp5aXmJ49gYvkYyTiIpzyjBvHLmbz491ZgEidJOCTQgLqNaFAkxRV+wOUkwcZOkQyJDsN+kMBXWrO1kYmXWyoVnbRwq4IMSfCIijiHS1GmT7WyUGFKakBiHlwbfzilKw6hK0XTEErqTHfry11jDdGeXcmPK8taM4yeHPDg559FyyZd7FXc2puxcLNgKKRdxFRNPUiA5v72R5NQaVUBJbD0rlLNJhbYBUcN5Gzk6vaANEeMshJYQPE1MOBBXWKalY1JNOJvV/Df/5Kc4HKVfUDQL9nYm3H7/OrHa4me/OOLioub4+Jzl8UN8c0agpSjHtDJhtLHLzf/ZD9nc2UhGhSTjKZLSbUTSwfxGm5J4WELIXt40tom7IhGgiXW4smBja4tVU+NjisC1PtK0EV9fIH6Ouu3+dGyD0obIqLDMm+QMKkONPb5PDA3RrOGMqYwsvABeCjyyll87ELabhsWZICtNB5Smym9C4gcyJNJiI6najQ9ZcI9HzAvH+WTEWevBCdUIptstt24tuXOrZlJ2tKbrg1XQJPg6gZgrRyWZmQ4TQ1qHffxQyAdKWu9Wyai49fjq+qvo0hzeRtPXPO5SCda3c9mY6A2mnqfr9fe2VmXTeJXOENomyR8RxFlMcAkZZ00f6XAxHaiqyiR43t802P2Cj35+zuGLFRIiD5aR/anyg+9dh2pEiFMm4zFiC4ImxTeR1RapuhoGBhB+Q04XHagaeulRZ2Slw+/qYF1JHnpt74ej9nZm7zXtlSjwqxD1q0aT6e+xu+N0DnSfR0yvLPQ/Q7YvrKAhVU+QjFrSfLaJpH3acUU5o2yNA9uTwD/7gxecLTonS1JOVAyPL+DT+zO+s3lBGG+zCkXaA/n7pEsVFekNfyXLdhIySCTB/C3kym1D3Vw7X0zfv+HW6w1NSci3KF2aBpklRXJk7m23Tp5I34GOWLZzMXbPhqbt1Vnun1+KbicBIyahWzREkHRmiqT89qIoUjQQQCOFK0AjRpXtMnLrmnD/8Qu+fF4nDjKUWaM8eL7k+rUl+3ub1GZEUU0Bgw8tErVP16EjjUQGEHguzwVDE5/M3/Oq4XVZNGqWtUkvWqeaDnE1sh7XN92y7EyPO+dM15P1LKWytenVKFzibk133ynkw71p+vfTKwaRmCH3kvhkQk4JEOkRK9aa/rNRJRVfEHASubPjINb83k+eUMcu9UBQI8wb5dOHFxzsb7N5sJ0CHAiCxdrswMhV2IDeodpbs9CnS6XHlyf3Kg6qf66Xe72+JvX5rSCkBq3nBgTWceivluCDJFm+ymOxxnUNZajBSOJ38prIUclzbnLQoShLjE2QixgjGpIxGjUh+Y14bm8VXN8pOTxvOboITCcl9w5GOITTRaR1FleAzQarNZIQqNZhyyI5aG1ypBojRI1Jt14Lzb53ZL1gzd0z1OG78bvcz8tpVlfOJ3i9M+UNtKG/qZMD63M7y9TX/LJcucf1jMp6jvVK/1CcTeSyftVAFQlBWcznzGh5blMFx41qzPWNDfaiZzI7p7qYM65XjEKLazzWB8RHMhEKvYQXRU1C9qu1RGcIzuCdo3WWpipoNye0W9vMx5tcuIraOjAO26akK2M7PUAH4zEUWF1f80rtHDeD87N7cOkTkveLyi/bJr96EyhKIXkuBW0UiAmZKaQCBtlhI0rmOBUIGYlqM0pdDVqA+JhS0zSBmPqzJusnmh0+aw0/7ZeUwZRkdl03HIWW8/kFk/GIjdGI6WjMeLug2plQiaFCKVYRbfw6Pb8owFRoNSIWJUuEOsJsuSS6xMWJ14TSSWYqiec1gw9YIxET2WzWv+J6sNYVadNcdBzD/dYVs5ZtEnN4PWdoXNnfX9V+RSLipFwWmvI3awRvu3xvSUZ6RpxUhdJEZdYEPIHSFYy0oFHQzHVSX5xjgqeoNhiNSow1BJNLJWqq+kJp2L1zg8nOBi+fHXJ8dMx81XBoLLdLyy217KpiQ8TaBMf23uOPTthyBVuFQS9mhDZS5XzRoB5vU48kBNqYUraubW/T+oaz+YJHTYNR5aJpsc7i1PDg+RFz37AwoBJpoicQKLp9Ir3oHAiYtMhjUDRj7QJC7X1KHzElIpaUH0nmY4AYPOpsH2V7800JMW0qY0gBo3wIdLIwcZ0IFwuPcx7vI8tliixNR2NGRcWShFAQjRSSS8HKmNZbsIlsihBwaPKcxoiEBCEVFcrpBrvvloz3d1idXPDg+Jwvjk4ZCVxzlmk2FqogUArRCKMo2Mqx8m1yjgU4NsqhlcS/sFXx5YtjLuZzIOBEKIyhQCgKS1WWXLu2xfVr12hkzNNTx8tnM8rYcjBt2dgZ0Yw3+eTlFstmwsXC0YQZxQTC0jIuBWMrjo8vCDqnGkVsXKJxTFTfbZTU96xEFe5XBrX98tlTZbFcMhol2DWSSbGwBE352RaPGKEqS0ajMZNxTfCR1reE1hOXC+LsCN3ZSMqLSQL2bNGyszVhWXskRtzFI3Rxkg4Ysz5IZOhVEMFsT3j3Ry0HkyXNApanwvIUVmdKcwF+JdCmyITDUe4fUG9uUM9mtG2LHxd401KWC65vtpixY//GBnd2jhmZBEU0+beUTiHQPoWqI/pPDo91Uky672xIRHqhmuRt5r4BOvLamJVzzSdlLir/GlPlLzyL3eAN/s1QS4ZGhF4+tF/7aQZw/bUpOUC+pzlyGXHYHYsKmMS74LAETVxCXfTHAgfU3Lo35feeznjyZEVsE8qpjfDF0wXv3Jxx496U82ZEOdpCbEnrNcHLrcG4ZIwmfSJHmLQz7DrD7arTomtC7NOjhlfSD9JQdc2fyOdRms91JKqTzm++dVwda9M936+s1dTueacgpyHoKvdokomskZXd98jQsSWdgprWqzMOKw6iUopJUaHsTJEElUtyOKZKbyMXubkNL18e8sWTJWjO7ZacUy6GBsv9Y8/Nlyds35vgpcLbMajFOIctEhrT5HuQ7LQZIk8070l0iGJIbZiUAtqnQnaFBtYDmta96UdVL83f20yR6hXJK/k1Q3NP+pWV2rC2nNDJoe6VjMIUegmjIqgTcIYYPSIuGfr5WxOhfF4rMUG9DUppIrd2lWV9zi8enLFsO0knhCi8PPc8en7Oh/ubFOOQgOliQeOldFNJiyjNm3TjeXU/0pPT9o7FDkkm66u0H6s174vQRRvX8k0urQLzZg2M3HrZKbkf/UEwMBZZ32u6fpg41M3tpW+8dPe9fEVIScrJCe6sSSmmkrgVQuyg8XnlmmT8xKgYUbZHht2NyJ/+9DEvLwBJTtDO+RJFeHnecP/RKT/Y2qQaTVmFDsWc9rm1aW7RtayIktMXhnukt5OH5q+u13gvYi7L4u7V/iwajMj6lTfd9MrjTi7qlV+H9Xxe2as6vNNX77brvxESibRPlaCMMcnJpg4XCsqiRAtNy1Uj0ceMzI5o9OyMI3dvFBydLfjZo3MuljAeFVhn+fa9LSZTx0WoMOWYoqgy56Dpq+9Zl2yAXiYgGRVlhrb8K/evl7qUhMsQBTecyTQe3Tgy0BO6z/4Z0/HnbJpvtAsEd3pI/3OvQSuu9yb9416q9Nt3eNKkv4ImRLtxtG0DI01ks8bgQ2Q5r1nMF0Tf8nlZsDGacG17n1u332NXPOPVBe78AjtfYlc10tRI8EludvLRGoJzhKpEJyVxY4KfTmirCUtbcd7CKkATDdQeY1ZUozJlBcj6TpOWIlmP7vrdj1jfs/VbwwkaXiP9S2/DTlzM5/zx7/8xQtJTCpdTaru0dlXakDBqo6KktJZ6Wa+LeuQUeEESett7Yhtpag/GUBYGG1KQMCC0PnFMUpmki5KC7pZUcTmtnLRPvfe5onHNYrUg1p4vP3/Kee0TObutsK7AlhWuKtna2eL6jWvs74+48949ru3sUdgCHxOC1bct9aphcbFgcXFBu1xg8DgLrgDJbKtdAKxLAeuc4jajSjWGdJ8GjEmoO+lQl130KVVA6vU4zfPdn6m/ZC5/ZUvStKlUtyr4rJlYTdDLlKMU8dGyUaRtt6wDorBROCpn8EGJzkAM+MUFzkTspMCWRZ++gNGU7pBrFKoK5caYW+/fZnNvi5PnRzw8P+dxaNgpSt7f2uF6C5vzJc63tDHgo4FgMdbknPsU5fUSkaqExqNSsCqVBQZpAmXrGU9GXMxXnPuIJ6USOR8yT4nQWgHSAblqWubLFUEthbXEGHHG4IpUXaiuPVEDvo209RLfrGhWK+YnjzBlSZQJW9vwwb0DnBYYTQd6RplhtEvJeLO70UpOi8pRPJVMJzxcLR1/ghpmXnhv1xFYcb70oDAuCpwtIdTY8wWbMXLj4DbbVSTGHS4uUhQfo6ivkWbBpm3YnkKjniZ4KuM4X7WcvlwyO59TWAvVmADMo+e8aXLOtzJqG7bqlmnUhK4SRVwukRYiKzEsTxvaEPFHR7QIpjBMpmPG4w0m4wnjkcMYKMuCYjzlVK6hdo+d61MK69DVCcbBcvcdlqObiJlSTAqubQdCc0Z98Yinpz+jqT1WDbsHuxwfnxHaM2JcYGSf7gBMZb9zFBXBujedHhVpmxZnDa5I0bmQ0VtGNMGnY0KCGWuYjCeslktWyxVVWdFUDW3bslqeYjZv4OwYLylFIrYNoS0pCoNeHMH5o0xAnSDEqRR9Skvs+mmLEWfVu/yDB4a/cu8+724ds7sTMSY7B73gFbw3NDHBHdt4RKtHGAVnI0UBZRmZlIHCRUK0nNYbGLuP4WXqd16i3XpNXDPJiEyEw5lHIUc6e6Ul59WqgFHTC3/Je0w0OW47gyYpTimH+bIZ86bb5RjhmuWi7y3JkSMDhFy3BrJq08uIq2bn0LDITgxjCdl4Q7rUBU3QfunuYK2ub4jy3oHlaQz89LNzwrLFDCCvF7Xyiy/PuH59m61NYSUFKlVKr9Q2RZasyeZmUkbT/WYB35VRvxIBhmFfOzeM9M6btUJ31bQajIN2o5tdNrL+/Jtur5oY3ZNXcVCd8ozqlbu/aham1kV5XjFW+uhpSq8RszbkNATUmH4PiEkkejsb4Fjx8WeHrIJFrGBNMiLEGIxxGGs5rw0PXrR8Z3dGtTEDKcHk6L7p0rayYZiNgM41Nly9l0MYl7swLKU9dBMMM8JUtau4+sr4vun92JvqXWQz27PpXOwIeNfjbga9Tcix+MpN9Q4qI0gmze+XZ6frmEQEn1/s09sMyeDQHGlEk3TYmnqm05Y//NOXHJ01JMya6fnzlkF48HLFrZNzrm9sUbOBj0nmGUNfpr3vszEZaSK5THQ3yledZLF/ZT0l2cDSgREZkzGiRunSiYZ7uTe65M3PYf8bJP1paNr16zHLn7V7ad3D7tOXUZXmlXuVvECGq1tM+j+EFquJsDOYxDHVfUfiM0gydmSVazvC4fFzfvF4ngjdxWSjPgVhjLUEsTw+XnH7+JSbdzeotUS0SIaUsxSFI4aQHUE5gCPZydY7YobmL/0y7O3ffMHQyL80NzpwAShr2TOY1jfa8mJ6rSEqZOOok39r9233PGay/U4+XgXJdrIopXWAKzNFQdRE1G0sasEYR1lWqCSyWQG0zClxUSmI3N23GF3x8MmC2SwSFRaLlkfP59zcm3LvYIoJE3S0RVFUoBBCJmItckADpatIFpGe3L1H0wwca30A4yvPstd0VgfvXfIEvW3MlAweXVqBvLp4Bue5drL06jkZ0d4JNBgWSYGHwhXEomTVNCkYl0ik0JjD56rUqxX1cs7p+TmPXz7nx5IKdIynU3Y2dtm88R6bGxMm04KyTMFAlYj3KYWn8ZFF3bBcrvC1Ry4ibgG2aHOFpQ5FB84JhUt8RdbkQDaSgjcdQ+yw+8Px6jaqDHgahwugRypeHc8312bzFf/NP/5pPvsFjT7LmMzf4gPLusFH7VW4PpVWU2l7MWkMBU0V2siV6IykwFJMeykSIWSEbRccJmJUcGYNiOgmPALGCtZaSlsg0bCqlViOGG1VbG9tcnCww+0713j37nXeuXWNTWupz2a4xTl2NcNWJZVxFIWj2Jrgru3gxrcxRUXjI2dnFzx/9IyHn9zn+NkhxBrrAmJjCgSrT2ldUTHGUlYlBJ/SoUwqCONKi3WOoiwoRyW26Kg0FO9TMR3rUtpkcvKkyoJf1X5lp42QBtgrSYhpJxSVaJWGyKJp2bLJmbSqs+OsMEyKkrl6xKRoRCSVJ54UI0wwxMag1hAJfURSTIIDh5gUmOnOJsV0jJ+tODk65ejigqPTEzZtwb5V9gU2QkqpqU3Ei0spXCawIEX5R67ABGERlZeTgpWrGLeRRixnF0tm82UadI3E6AlZ8bVi2R6ViXBzNCZE+Cf/9CPECttuRaU1k+mE69dusLl/k88fHnFyMufs/ILV4iWz42dYa2jDClNu0OgGd2/d470bU8RVWUHNJLeSjVDT5SG/qTbIaI1ZLevRp7oWlBIhJnh6jWVjc4wt5swXgdBGSpPyeFm2jJ8d8vUbu2zc+4BFCy9ORvhgCV7yEbTCUuNtQCshmhYr6eAqRg1qj7DlOXUzZxVTipbSEMSgGoh4LnzksF3QFcdDyHwr6dAxkog3izKRLG5PprjRhNF0n42Na4xHEyw1MS7BCitbgewxLm/gignjEo6ffsZq+Zwb1QTiDYQFalpCbFkcfc7y7Eua+RnBt4gpwTmmWzu0iyNmsws2ryuqyctqrUtOlAxN6ZjR39gsahIKnUtBOgUyRkLaLMlZohFCitomcugEvzXO4AqLW60IzRlhXKG4pOhpZDFfsTU2rM4+I/gmCdgsKDsbJjWDtZYwvc7KbfLw1PCfzb/Dd28c8dt3vmS7moFCVab7s1VCZIEQqHtDyUXpD7uOuNQaz2b5jIV+h5FtMf44lYPPJ3UHVO/OvUji1iJ/hcl32xfv0Q6p2OVrSH/mdSkb6bqcXoXmfNi3dSheVp66Xlx+f61RXzagBm/nV3rlG3qDor9ANMtUk6rT5c93NprK2ujQmPpeiXJ7y+B3LP/snz9icVL3Jd5NFz1BeHoa+fLhGd/47jbRbNGEERqTvLQiqbxjx+CViU4T91emrB4ooZ34yT1K/X61s/meL4/iGrr66jxdcie8hZBUh4oy+ec75WV4x69D+fSJNtqps1l57xAsqj3hbfrAZUYCKwYRS/CZMVFS1DgakwjtO+tEldIpWyN48OiY58ctRixiDEXhmG6UKNB6QBzeCE/PlBsvZtwanaDlhKBV2ja94ZuUZ5vJ95I87pTzrn/re10biWuEyqUly3D+tUcvkY3ELkKrqpftjjfULsV8e9tGLlm3nRMmAUgGZLxrbRKgY8e4snbXDhuBnCaa0FFBIyqaiKSD6QkNxXaVpdIaGTnlxn7Bk+dH3H+8zM4Yk1NATa4gZDhdCI8eL7lxMKPYWOK1BJPKm7qixFlH8CkA0+kXyekzMAQuL96U1iP0i32YImZ6+XnZS6CmW/Vpz2v3OwwdHm+69aOf70P6NI1uO/REAgNnRP8Wl2XJFanTX9U5fbrXTeZm9L6lq0oiJkWljTW9mwhSBHZzakBX/OLLY+arpKwnB2qC82NM4gOzhnkN95/N2No9YzQdo1pkB+36PoWkE4gVos/9H7Zuakkw/jSHwnrKlGEln94olPW4dGa3kcsOyDc9kWs0SPf9g42j/Jlrp09BufQdDDZq+hIRUiTfSdL1NVXyQjIK11lKU5FQaglR0AF+jCq7U2U69dx/OOPlaUuIOdUuRk7Pl3z+/Jwb17bZ3xmx0BJclZwxbWL7EuMyYSprXb+zyTtk3FBKDoahv5FXTpbLCJth2t+aY2oodUl78y3IVNAr9sv6RNDhwT+8nyG6UTpnwOVTvivE0HOjdWvVWnAVTRN7WSWkFH0rYK1gC5uoVEhOMR9r2nrFfHnO82dP8V6zQ04ZW2FSWKpxiTORZdvgxSFlRTUqmYxLpqMKKcaISfxjTgxiLIWzVKXDjSrEFYizKasiakaNh17/elVnWY+B0KXmd3v1K+SVvvFtiCssd+7uY0uXUPpti1EhhARqWMzmEC1tnQLmMWbHfj4vO8cjMQV5k/6ixNC9NyxhQOIn7lG22p+5xlqwWapqcshbWyBlQWXGuMkON2/d4N3bB9zannCwMWJbldFsgTs6xX36EDNb0qxafOsprcMERVJeMbVR6qIiTCpkewN7+xrFO7fYfPcGB7f2+fCD3+Hhs1N+/599xE/++GOOXj5jvjrDa4PmsuZdN0yHyDNp9px0MZe8Rg19FSlBUiXWDo3Z2+BfPZO/mtNGwWki2lIJFERiNEQKchybhQZmjWVzI10/WwWiKpURSusQDdhZTRGVaTlltHWAmj0aHSNeMR7A09Jgw4oSTyERDS0SQIPiVy3ni0DUEZOpMLs442i15FiU+wgTwLWJELUpS7YjzCGx7odI4VuCehZWOFZD7T1jNcxOZrxcLWjaBlskY3RjOsKIpbKO0lr2djfZ2bnFKhacN5aLdoKrZ7giUm5ex7trPJ3v8mS5yWy1y5IFVM+xbcNkfE5RCHXjuJgtCbLC+DGIpzUGlchIkqLTR9iGCXFvpKWyzKkUp01RzABdKoEimQU8Tbgo1G3EFslb+Ox4xuHpGbNosBZM21DWh1R2jJlsU9nrHIy2qGNB3Sb4aPAtEj112/DgoqH2PimavkXDko2tXcajc0I7pwkNbWgIoQE8sV1BSN7MqAGNAXJU2BUG9TVOIoWzbGzsUE0maV9YC2aCFDtIuUdLQRPmBG+QEKmjwZaGeuVRXVEaxTcFi2XL7PMXrHQTHy2FazAxUHGMDecZ7lxQVCNWqzrB1lXwqyVojYYWsnHae0uVpIS9yVnsDt+Yogcp4J2U1RgjIcYcEY9ZziujyZhyMcMuEqoikviUWBxix3tELZJwMdD4hvrFA1ieYiVVhguZM6o3+DVF86TaoR4d5MiGsPAVv//0Lj8/ucat0THfv/mM9/ZOsC6lswUSJNnk8zmq4gGn3UHayS2hMgHiZyxGv86UPyXGWZ8uYVQydwCZwwMkahKW2qVnSHZgKKLJuEnrOjmNtbPANIMVeyMmKwW98dY/eIPtqmCO/SuX1bCs6l86uPsLWRsSVzXd/oJs9OZUspyHnDhQUkqMMQo5SosBh7I/UjYOCv7Jl4c8f3iBC21ftrBLizHWsIqGz58suXXrjOmNTXwYYXAZAp7KC4eYDmubjUoRzal2mQdl3YPX3f369cu65iWnwPAbsspOZ1r1ymCv6L7Z1kXM0nLK0aL+3bWS3BHMpaW5Vq57KT9QcLs7jXnKO7I6HVxhc/URDdkk7JxvVkBsPySWwO5mJPgZnz85pg5p7qyzHGwX/Pp3t6hjy09+vqJu075Z1PDwRcPe3ozpzXMumgmJATiX1+zvS7Gac88Z/p9uOhnqZDRKNx5Ds324RhmgHHqM2CtrI62Xt2Pyd1MgX/kLst5qV5To7pkMxyAjc6G7b+mvseJQFULwSRvLqKdUca/TAdL3WKMcbFs0LPnZp8fMVx2vkKYUR2NyRNSh1vDkxPPOiwtuTU9pTAU6Tek0xmKNIXR7JaPsYszRbelQfd1tZ7RTZyD2DpuuT/l78rwNuDPz+ugQTHlUu+/SqyP3ptpg9w+5IrTbd18taWT98UHrRqH73i49Mb+a96SR5GzxIVWPiqSLDImfpktHFSKVi0xLePb8jGdHDYjBuqQnTEeW67sjFo1ysRK6FLbD08CzZ+d8/b0N1I3xWiZDv1trWV00tiN37zqT/u9krPaPOtSUID1ydWBUXxmhoRN6LW/Xz95sk358X/uL0sn2K3KhW3eX9uXaebNGznXvpPPMWUdXoS0VHrj8u8YkNFvsdA+Byik708ByueDR8wWLOvQ6hUgq4PH42YxPd075wTc3GZVQA0TTy3qhLxfVo4KQ7Lzrqpl2OsrgfoCBM6Zj5xv0eAjD6cdk4CgZvJHOmK4oyZtt6zl8RWm5fHPDawaOq/7yr0ztWl/iEMYuBR5901Ca5Fjs9p/NVTK1LCDmyqgxGdiqqeiImIgxgU0XuLtjObg+4jAUPHpRs2OEf+nDfWYx8uiwJahSFVAUBtsT1iY5bK3FFQ5bVYitMLbA2jI541URDUnf6pSGK7pdmpMsbzKa/CvHOP/7Nk7Dqii4dfOAYlQRfCpOEgP4ukWMUC8bnrw85nSxogkB70PmllXIFYk1ai6UkhZzjJoKGmU+zg4Vd0kVyGMhks42Y21KMSOh32wxZmtnj2vXD7h7+zpfu7bDHSNsn88pPn+Kef6ScrFiCozUpMIPAl4CjUYqEymNwZlEuq8RqAM0Kzg7J375mPaf/gl16VjubiLv3uba1+7xP/+Nd/kbv/NtPvr8MZ/+/D7Pnj7l7PSIVbvCFYJvPcYKPgQCWW7kgydGJXpNROSZ2McYg1hL8D5JaWuIGmjb9ivn5Fdz2iRdDqFj2CYrp5lyVVIpq1YKdvYrRsWMedPQhoA1hnZc0M6XTA6POSjH7O3dZnN0hydnE86WSgwRpwoaCMyp4hk3rpVMx4a6jYSYvNOHZwueHx+zXNQUVhG3gQ2rhMhoamYKMQbsKvBk1VKRShI7a1OFJ5PK+7UCoV7lkqmCWoNxJbaYMBpPqaYbjEcVYxuSQmotFCUzu4cf7VBtTphKSVg+xWpD3Poaq/FtbDGmxeKmls3VMWHpOW4eMSOyWrVMyhFbGyUXizNoT0FbUJe1ihQ5Teaw4VL+5xtqoibDmZMykCLwSVaajPZJfhzFoVRdWZ0I95+c8H/6T37Mcu+A4+gwywXXr28j1Qbny10mW7cZT0ZUkirEWFVi8ImnJ0Z8bIghESwbP2NTXrI9gWUDZ/MtzheWZahQKRCBtl6goWVVzwlhhY9tUioxFJXBNxfQXlBZGI92kGKS0j80gIxo/YigFisOoUJ9hXOBGNK4RlpW3hObiI0TfHmb1WqDVRsIIQnN3bGyvWlpm4Jius3s5DnLw1NMUVIUFTFE2nqBCS0UAIq1NgmCPLDWvtlZVFVWy0VS1K1QuAqs4GOdofiSyzYrOacBYy2jakRZVmkvtD6RJi5m2K053lVJkRcom2PC6RMkp11AErwpjXNNyGWKMfX0ZqowgyFKUngicDIvOZ/f4P7ZAbe3Z/zarae8v/ucyrbp8zEpC0hKy1LJTAcx85iQqosULGDxc+rqmxTxJ0RdQZ49yQtTkFyXV7Myk8ZAEOKAiHgdg825471CQC7lJ9nozkqhdA6TfMNv+GTszfnOkLiqrPSvDRWfq1bFWvF5vRrdKZ4WjYm1v+dHy04URInG0M3syEb2th1fnC35xSenmLZBTOyjVclxk4x+sYbjJXz28IIf7l1Q2QmqW4ixCSmQoaDEkA3LhLQE1hD7LmJGZ2ilxbaG3OYeXtFFXxmubu5fwY5nwtDXjM5fvK0h7N3qoo9cDhO0+nfT365vIpcMia6l4E2XhKO9HdZ9rtPxrXNEbdL+tA715HMk/TVG2Sw8d/caPv/8JScXDViLFcOkMnz9tuWvfk9pjXI+hy8eGdQLQeHZReDg2ZzfvDNnVV1QtwUSbXYy5bSYvkpEl9q3Nu+Gy7rnpND1i9LtQZN8Qd33Qo5ODaYx9tvjzZ+JVx0InZOpsx+kU6hf1S/7RzpYkckMfnU/Xtq1mpymxjo6f0lHtBwgpy/ncYrKpFImZeQXnz/n8LhGs3FnLUzGlu3dKcenK4QEv15G5fPHc25cO2Nja8KcMUjRz1vaEwlfiTF4DXTVJC/xlbFeu0YH88pgpntuKb1sX3bpXnnhCslv3qc3/HefoV+p9bcwmMPufpN7s+NkuuySWd84fbSY/EfJsP7BfPefl/S8LAqC71LpUiGCYARixmiJ4oC9SaSSGc9fHtE0irMOMYaqMLx/w/KXvz/h8eGKf/GLwKJxoIa6gUdPl9zcPWP/5pSzpiJKQVDJ1fjWHD0qZMLw3O9+vHtLv7/33gjuvZC/xEDUbB72zm/zlde+0ZbPwt71dOUcSPIwz4uuX+v3Y2/sX3bciBisWCpXEGJKbepQjgI94apIpirIAQ8xsDFRrDTcf37Bi5O6L4u8JoMWmlXky0dn3Lq+y91bG8RYENRmnTBXbcwO/oS06WHvfWDkdbLj1ab9v2t9hfV09kbzVwytDj/0tlrnhIjrfl4SuoObluFLOnypb7GTVXnviYGydIgpWCxrxoYUybOSuOOcoSgsQpn2f3aotq0nqk9OOx8pabizLWzvOp6sAn/08SHNhaeUyOLshL/22/co7005XjgiI0pXUbgSEQciGCtUoyr10ThUkl6lg/2JGNTYZNiT+3jp7+CUk0v4t1f+DuXWm57BonBcv74PJF68pvU9WqhpVoRN8MYiJ2csVnWucBjwIaAhJNso5JnKTpsQlRAjIQjeJ/28l7OQnGf5mZLKbZdlkXhqXMXW/gHvfPAeX7t9i5sWdi4u2PzsIZPnx1Tnc0ZxzWOa0rLW6HWnmQok+Ixx7VoKRA2rX1YijJuIPj2kfXJI80cfsdrfwr17hx987R7f/q0PebJ4n/sPn/L88JBlPUeswTqhWS3yGlfECc4Yoo/4mM58K4kwwBgBsSwWCzTGlF5FxPs35LRJh5b2lUEKycefeIxEAslpE1qldAWFFS7OZtSLFSqGaWmJ2lItj9ie7nH7znucnCvVxg7jUUGIFmlbgq9xEtBY82Cxws1rVJVaHNHX1PUKGW9TlAtCPSP6FaIL2lBjRhH1NcRA0EiN51xjgqxFUGMxMT83Blc4qnKMs5ayNEzGW5hiQjU+wE22Ka1Q+VNCXOKNpcUS44Sg21gdMxkJ54fHnJw9YddYHJuY+QpcQzQWOX9Mc/6Qi/On1G2NBbwqo40tVr5mVTfUzYqiGGFjzpMzyalijOtRBG+05dM6oh1cAlEoNFU10BxRtwLXC+XbBwmBUq9alJoHR4+Zq1JuHVA2yqKZ0sqYk3bC9QZGRfKwBq/4EPCtT2gK3+Kbhq6CQSE1S9OyWi4YlRc0yxU2btEuC+oA1hRE3STQgi0x0uDUY53FByFqwDpHElaBoGMkjPAakgPKjCnMBKIjRoO1mT1cPaoR7yNNaKi9BR8ojcHHKa0viJlAEIFr1y3bMmful0hlWQCND0CbDgQj1E0D6lFtezLQLi1XTM5dfoNNNXJ0+ByiZ6s44OL8nGpUUVUV6iLLxQrfeCI5TzvVsaKqRj0B3ubmNucXZ8SmQS8OMbs7qLGMpIWLB2ioe12u957n0RYVClPQTG/S2o30qnSGqkWSX44owioWfH66z6PzPd7ducNv3HrA3a0jSuNRE/sUgpg6BtLFs6RXum08xrcbxNGH2NWPgdAT20o2FPq4W28g5FSqvmyw9m/r4IzU/J4lEbdKb1cOUjR4G0divoGBs+ayU+GycvqqZQmXLr/ULr+YdB+hdAXR++ScRXEIakymSE//FSSUzUo8H//ilHC+whIwViiN8N5OyXhieHge8VoAQhDhwYuWe0/OuPX+Fl4nqDg6AjbJE9mhc4JmR450juk+ft2bvubKcK+7e9kh8IoJ383ZOkyZX75MgfsmW2fD9D/NwKmkg1m96pzrlHLVHJnvuiDrYHnXNLGXILoeA5Ji5f0SwSPiExFejDjrwBUUBdzebsCf89mDc7yWWGsoDexvWT78uuXWwQpjld/6nuPFccviwhEVVi18/nTFB0+Pufa1gmfzCeo3kLjeZ2pMir6FSNCwnkmFvkRyz5F2WRntp2foDBkM3nA86dKi9M8yLf9iLRm2l3+hd8VIf0Vv+b1+S675UnqDWS8lOiTJJUpRlMSgFKqIRNQKFLZHPYgqVaFc2xXmixO+fHyWiidkZXBcKD94r+SHP7rG3/5HDzg+Tb8eEZ5fKF8+uuBbH05piw3aWCTHRVagM3QuE1GbTJh7dWTX+62z74dGog4n75WPZfW7c35nudz7v1/nrXwjrfvdweNOnvYwfTOYvC5Bdm0IDr7mihIm9HgbHTj1RSirivnFDM1wd42pgpSQovEYw7Roubnnefn0hNPzGhGLy4i53Sn84H3HD74WuL4TOTyJfPEMfExn2cnMc//pGbfubjOmZOaLVDlM6HlsNJDDqLkD+QYTkqZjLBswiwj9el+zFg0l8SunUn71bWAzhk2vPP4lTgegr5A02G+vXHvJGZAeGEmR/FWbkBeJ38tiLCnwZegDnEhCnY0L2Bops/OaL5/NWTZpbXVp5N3/AMdnK35+/wU72xM2piU+WpBEIO3KfH6GLl0qBY1CfM299+OwfrQ+bNbXX5Ixg1RUGQzA0NC/PCBvo726Sjpd7OpV/V0MWJYHWzhd1+lwA2O8EzXGWdRY6tUKSwrUq3UknTQ5yAtJctcAErsKiGVWPSMHZcm1PcNZ9Hz+6ILlLOlLHnh00vL5/Zd849s3kdKxCiOcm1K4EVEtIVcWwxRYl4uEZDkfpUsVimmnZT1zHRy4zGNyCSw19IRfVQp+6Uj/xZogiI8pldoVWGN756Ixwny24Nq1HYwRDk/OaHygDSHxvLQe7wNiQpYxqSCPIaagX3dKdqULc29Mh/zUJIuMsRRFxe7uHvfeucXX373Nu9NN9k9nmC8eUxydMFmu2NCku6aqT4kePgUmMxZREketId1/F5joJaHkgG1ndKuipAqqTgTX1MQnz6mfvqD96Be429e5+7V32L93k+P37vHwxQsW8zkxNLSrEpMD9aZI6NZOp8tVwTOxcqIsWE5GOfslobB8DF85J7+6JSlKYUI6RKIj5AIApSrJN5QWZNLTCh4+fsx/+0fnuOk2/mLJaLlg0wYmxnB4GpjuvYfRHSZhlMpLt57oWyREvCp1aIjtEieREYLxF+xunnNzf8GyrvG1Z+5BfQRj8dHjV+eE2FKHJcQlUT1OHIpNxEVhifELcEox3qKqtlAMRlsKVxHsGNwuMW6wwuBbkiPAQu0dNkK7bFANXFghNhV1qyyfnbF88QTbVli3THmN4QSrF/g2QITCOZxYFss5EcOqbVmtaoqxZmU0V8ExNleOiG92I2o2ziQbuiJYEqJmSmDLRqbjyO5G4NqG8uGtCR/cnfDffr7kwiu4FVYCMbTJO+0cz57VxNNAY+Y4nTAZezRoYglvWi4uVvjQApk9XBWs4GzkBIszG+yMLjBxloz8xlH7DMM2QiBijWApUApCdAnJoj6lykiNSEBNQugY42hjwMaCkRshpsQ3EQ0CtgDrcKL4YNDY5bsLyZtcYmKBjYJY8GJ4cO741q172IvnnJ+8JPgGK1AKTMpUxer4aIURm8u1p40f8sbroppvssUQcWXFZHObUTWi2NpiMZuznC2whWOyMcGsLHW9IoaAjz6jTBIkr7AOX0ZG1QgfAu3yFLtVY8sJ+/GEk3aRUTMJvtjpEELi0bDGoKM9FuVeikDllBn6aFTM1bMswSTKNR8Nnx/v8+h8k3e3z/jejcfc2T6itC0dIa5mCK8lRWiNSEZ9RSQ8YBl/nXH1Pnb1WRrXTPwWJX+mV6Y7h046/FKqrGQSyKQKdPw2RiWRMMvAsJTLboSUamVIuYRvtvUR6n6E+zdYj/rQsr3MbzOwFH+prBBVRpsj6nDCmKTgdukXXYUTq8p+obx73fCTsxNOj09x+JRKI8qNEv7q1wwHHxT8539S8+h5jiKJMAvCJw/OuXP7hI1xwaytQGyGjHcpn+laY2xykhnpD8mhUWAG4fG1IjdQPFV7n0xfXUq6NIzkDFqjHboEAK5EV95kk3XEj5yC1ivuOrzq0qe6SlDpvaGyve70UMFep9bQK+CFdfhGIJrkIM/V+awTbGXZHHsOtjw/+8kpJxcWaxxGhFEhvHfT8u13HJPCY4AfvGN59O3A7/9xILYOBc5X8Cc/P+dvXC/Z2xhzuhwTGpccTZoRFiJ4Ykqh7Mag2z2ajZ5h/6R35/RpX93rXdciPb1VP4ZG1yblm2565VlvuvbO6qQ8J6No+PtJ2V4jMLq51HUKZv+pNUpRAIkwrsZoCBg6tykg0ue9G6fsTj17m3N+8vAFi2XICAGlcML1XcNf/dGEr78/5+VRyT/8/ZbGjxCgCfDzR3Pu3Llg++6S49kE1YKQq1UaOl6HtIeMlUG0/3KSV3fTrwSRlDUfFZdX+7rEtAyskA7tctkweXNtyOmh6xsSHf4Zui3y1Zd32vC7ujm1+VmH0ByuRVGhKirO/VniktPM8RCT8WjLErFwa1upWPLg6QVLbxDnsCKUTrizH/n+t0dsThusFb79nvD8tOV8mfZ+HeHL50vee3rM+18b0Z6Paf2Yvrt5MjSC10jI66lLvxz40da9HQ7RsEcyHI98aYcKGYzaW2ndjf6SBXJ1XUI2gns3xWWZetk/OLjCJBRyqLMeYm1CiXZFCPoqTt05qWyMDMKKB8/POTlvelnfOT/7HzZKJPDkxQVfPjnmO+9vMLIlq4xu6266MyqtpNT6XgfJVr3kpPB1Py+v3S7s8RUDOXg6lLNXPSFvZy716v77KkftUH8ZyNxu5V5ODB7efeYGQahcKpvetKuMNkuI0874ty6h1pL/TRNvZ+fUEqhiy439Eu88Dx/POTyqk5VN4kNdePjs6YIb12Zs3xhRjSytlFg7QtUibcyVANcZE6k/ax2nB7QNI4dwRRoNZVAXwOoGqNvJa2dD0o3e/PyJJHRKNSkIIrhiRFu3tE1LVY1ZLppMTZADguRz3eSAa5SEIhQlek/boeJDJBrtORKHh4GzDmskFz+xuGLCzXff4Qcfvs+9Qth5eULx0WfE8wvKZcso5kwaMTgRUp1FwWbHksg6AKgGJK5T8ondeZtJoYlrx692Z79mDt7kkLNBMScnxLNz/IMnmDs3cN94j51vf50zhecPHnFxoliX7FxTFUkmxOTU1ZhIxn3T5EpchqpyaTnElLTcNPVXzsmv6LQRVBwqGRHiFcl17EPu514RubM9omlatGmZrVb8x//4D1nt3qLY2KGqG2yxhamucVjvsTydpEihKBBwIXFPqEkDGcWRCn55fIyU7oKDcsH2eE5dtxzPlBM/5aTdgDBiJBBHS2JsafwKH+ZobDDGEdRSYTG6pAlHICsKN6VwW3hb4n0NlESpaL1gvCLO4EJBNBVGAxpLrE/lyFrfENqIMyVRrrNs9miCw/iUalAUDfsTRYNgR5vQNtSLGatVjakmBE1ly+hIJDVzkkhCkXSRkTepoArKhEhpImOn7E0M1yfCzW24u1VwsGnZmBaMCsU5g6tGzFvDHz6ORDGwPKaIAUILorhphR0JzSyirma+XNI2nqaNuRRRJPiGGJrUP0mM4gQorLC3UzKft8xmKzbMKRubNTv7JQ+ej2mapNZbUrlaMYmB3ViHSHI8EIUgHq81asqEbsrIIYlKjCmKIjmyjAlgUyWA0lqIkvJaBXpqSDEUxtCGtOlWM+WzL8ccFO+hcgzAaDTCOUtRjhBbcjarCdFkMqCk1PkYkzqfiVffZFNV9g4OcK6grWumG1PM5ibBj1g1DavlkhgCmxtTQKhXDatmhfGGUVnSVBWrtqEsC1zj8HWNrI4owoLSLjDGpsNHE5zVAojBaPaSF2Pm4xtohoR2iAmDJCLxHuq79mh3Of6LUPLRyQH3z3Z4f/uY37j7gBvTo5wOkWCunmwEqfacHkrELH9CvfnbjNwx0R9ju4pPQOiHWHpHTb7tfCZo2lsMk07Sl0v2sguZE2fA19/tv3V6xptp9+/fP/zf/m/+vS/f4Ff+Cu3/8ef61CfAP/rJV7//B8B/+Pf+XF/933d79019UZrHf/d/oHn8i7U/+mP4v/6Hf/Z1vw/8P//OW7+dX7W9wTn84vDf+3f/nf+/nMOfAf/w//vLr/kv/4z3/wdub2weYehM0CvmUGrJFM8utktk9J20T46m3qTqDEhZO3fWTFnaf48hBeU0prMm+ERMCxErlsIaRqPA/mbDwwcnPDtXAgU2I53GFn7rmxU39yIQmFTCh+9a7j+u+fhLwZOMovlK+NOPD3n/nU32NipOFiPaRjM/BGsi3UjiEutQVeTEOyVziWUnzYBYem0IkoMUXSBkPa5G13xWqXdvoXU+BH1dquur7sF10yt/r/K4cNV7k5BmzhJ1SdTUm5SymZBn6TaykaeRwgbGReDo5IIHj89pQ1obxghjJ9zZLUEjzy48da4ItmoCnz885mBnk1sHI9QW+C5IlCsl9AT/kvkiTTf+nfM0r7ir/RlM2WuG8fVNeXUY3wLqLQ5Rpt2Dy74Ghv/27tZ1/iXrzbju95WwVX+ZtRYMNG2b0fbdvCXj3RqTnDaGZLBLVl9z8PpaBeNNx+eHSx4+m9GsAh1XZPKzCEezyIMnMz7cmjLe2cSaVK46aqrgJ8aleczzl1BwJiPw6UtgJxTy1aq93Ti9zgWnA4/P8FUdOG5eefsv1FTBFg7o0OzCaDIBnSPGMq4qTi7mOTMhI3AyjNJYgy1z6qYVWqAgEkicX5qdBjHba0Yk8SmaFKzwAdxoh29//zv85e9/wMHLQ+zHX+IOT8EskSqgTcoiMArWgMt7yojkAE/OsOkHJ6dwJyWfDn2ZbJQuPSqNvxiQID1Bf+84tclWt8FjLi7gFwvc42eYX9xn+q/+Djd++F0efHaf4+fPaEODiUo5csToMSGBS9QIziTnjLG5LLl11Kua4Bs2tja/ck5+5fQoJGA0IupxeKwJ2CKllmyPSr53s+SdaxVPT1fMliu2RZEA4iM2RoLCi1lgHjwndcvuxpK9TUcUqOMKVwdWq0ReW6thGcGGFU4jQQRrlOOiYLeMTN0Fs7ohastyJWnhkBA1ohGVEUiBSIspDBIt0ecjxmzgxWLMmKCOVoWIoxCLkTFBkr9OA2mQKWk1YNThfWQVmkSgFAIaDY1OWGiRCkqoEKKwNRJuXQucvTgiqmFlhCZ4QoiMrMFl8sC2WaHi0RjpKoesIY2v5zv487aJDfyvvt1yd3/EzqZjY1Iwrgqsy2DXTI4VgifEwCrAP/z4gp8+DIzaBXHxgqAtbdtQCKgVKhdoaFO9+9bTNsqq9j3UEwLGJo0iOTGSeySqMtkuqKaB44cN57MFMtlmtbjGKhQ5QiQUtqAsBM3M/salqjMRwdgSZIJEQ5QEOxURjAasaPqMNRBjivBn70GImvl7oJD/X3t3ExtZdhVw/H/ufR9VLn91T3fPxBkFyDBRECNNozABjbIwCwRss2BDgJEi1tmwZIMEWUQRK4QE0ogFEkomC6wIkJCSQcooQ2gyA2iSiKFDuqfdH3a3PW67qu2qeu/dy+K+Vx9u95e7WuV6Pj+pW5Zd5bpVz/Z759Q555qQSJIqf18O6RTBFyET3O95trIllhu/gO/+lML3iG1MN/eYJOHAF/RzSGKhHFs/rJGSyR5DgN293a03/+avJxtk3LgOwLXHvsPTPXwX+Bh474nu1QO++1SP+5QmFmB4789P6nup6dHjOPv0GNbDRx9d3fryl/9w6sm3b3zz7451v397//Fu9z7w7X/5/rEe4xmZaOJtaDx0rRJrYwF8mWuqrrFk9PNHJXaqCqmy3UkQbGRD9Z/3IaFVtThZW7ZvhGvWCMeFeViMMy7f3qG9P1JlY4SVM8JvfS7F5T3+9b8LrrUtebmw3Xafqze2ObvUotFM8S4eVBaH5YTrxqrKpppnMz5DaOS1qJKID3kn6XD1xn1fHMt8TLZSI9Qt+JEB9YNiipAIHDzv4UL94QSdlN/pyKWNp22qJA14fBaG4yNV274vK6GqTRBCEFI4VyZuhDlxvHChxZ1uh2u3O+zu9so30ofr9CJ0c+HaVo9zW/f4ZGufdK7FvitwLlSmhh3gqrWENRozUokO5RuaVeqXsddm8OI9zgtcVoQMEhATZqwlShPipEE/KzfvcG6wu7LB02gktLsGiS2JlJW/WY4xhjzP6WcZTjxRHOEsSFFgCZ0Kznl6BwcUhSdJ41BlL0IaN4jmz3Lx1y7y2ovnmP/JZezl65h7fVxeQMsgCwbTz7AHYU5Y2H2patoME8WcF7zEOBPhbESO0PcOZy1WbGjF8qFFyziH9Q7jcqTIEOMxKfh+l2owm4kM0rIUBz04KMJ8trzAtu+RXL5Gd6eN+cJFXnrlszTihFs3buCKLnGRh8IWa0NyzxVEHrLCY42QpilJo0kUJ1hx9LvdBx6TJ0raWHEsJF0WYuFcM+d8w3NhKeHMUsRi7Fmeb7GwMEcjjvnZxj0OCoA2DQp6QGYiTGOOAxy7H/cQ6dKWfSIT3grPXIbkBfcOerhuL5QJATIYPutJU8N21mSvs8RyvEcadfHiaRDRyS24BKyQeYfxOXHk8TbCEYbReuNwkhPlKZ4MTIIzKdbFmKJHEYWdeBLTxOWGngtblGMjrI3KmS8F3oQSVIdBfIyhibiI3PtyGzdLe99ykJ1jbu42d7fW8UUPjyM2EcYXNGxMP8vZ2884G9IYGAm7N2VlmZSM/MGbhL3t9a2//PM/OvZFzY2JrST4r3cn/A2n6JvffvtRN9GAXymllHoG9LxYF0cHoEcGplWRwkgFx2h658GPUA3FdVgbZjhSuOEw9PLS21iDLd9nX0qFn38uZ2v7DldvbeAkAcLW683Y8csvNXnls4ZsHza3oH3FsVuEuTUuK7h1u83myi6fXlwgyhP6edX2FColXFWpPXgPsdoUYWTW0CBZ83hB+uHoYaxeZfJx/vjjDgpIxmtjxlJQI2sYTTKNH8Gqps0Pvn446yR4jC1b4V3ZclZWLlXtU1U8NWgNL1uqY+C5Voxtxly73uHmZoe878Z/hsqfh0IM2/uO9c17LC23WW60iEyTvEgwJiSGjBVsVLW8hMSVmOqNXD9IJlWVYI8T4XlGEjujVVJV4qb6xASPqfeOSATXzwbJDW8MjdY8eb/HXKuB62dEe4ZGnJCTh52QrCFJU5J0kXudDnleULgcD/T6fbIs7G5LJPjChRku5ZvqNm6w/MKL/Mprr/LqUpPmpQ9I/2+dKM8orMWbAvIcsPgUbM9jXKjo94SOmiJKca0FsvlF+suLFGcX8WcWyBLDQZFhllqki0vEcRKqbLIMv9/F73bgzl3MnR2iu23S7gE23yEpumWnTY7v52WBg1AN2fPeY11B6842ve98n3zrYz75qxeJPvUpNjdvkvf3yb0jnpsnTRPyfh9xBXNGcMZQuIJe94DEmJAMG7Qo3++JkjbF7vrWxj985aMN4PJj3ufSkzxADV354aNvc+mD/33UTTTYV0oppZRStXdfdfJoMDrWb3O4CqUM70fmot135+ouPgTPVsJues4VZfLEkecFeI+NIpIkJo0KPrHs6BdbvP8/t2gfROVAVogtvLRiuPgZw9mlPnkTXnk5ZbPjuHwH+uUclV4v5+ZGmxfPdViYS2k7S9+ZwfOo5kk5F3bLLfdbO9QMdPiF4oGB+lEvoRvMF/PDlppnxA4etUqzjM9wGzdaOTT86iCpNZqwua9NKFTaWGso8BS4sMmFhMoXEYuXamcQP0zYmLD5yrzxnD/TYH2nzdWbHe61s3KnPBm0T1VxOkboO7i13ePC7Q6thTbpYpPcNBAfEjc2irBxjCsoZ9xUlVMyHA499vM5+pzvb3+rNiWonvUgIVZ9kpHqsQmzUehgKPLQKGmrf3GCx9I0MY0kxkSW7kGGT5OwY7B44liYm0vxBfR7PZx44iii388onEOsgIvJMkEMFM6y/MIKr73+OV5NLc13/pNkfYMoOwhZVRuBCS1P2UFRthB6ICGjQX9+iWLlebKV5ylevIBbOY9bbFFYg/eeIutx0OkgSUw+N08ktkzcDaeOWgGbF+S7++yvbxJf2yC9sUm0sUHc3yN2BSIFmDIR6cuqMR+6OdJOF977Ef1ulxde/zxmZYWdnS06+3ukjTi0VtmqWyQjTlPEGIyxFL0+Vlz4O/QAT9YepQG/UkqdaiLyJvBLwD977/9s2utRSilVD1evXt164403pt7i9qQufwj/+Mhia/jhJfjWt579eo5pom1u4hludsBo4mGkQmakUkoGCZvxeVTC4e6tYYLDV/+LIJHFiaEoQhA9TLiUrVKm2na72vIbYg/PzQn9uOAnV++ws32AFFXHRGh7S6KQDHLljCJEaPc81293WV5q84m5ORLboE9CtTORK4bVUOHxykHThGqKsfSK+LHWsYHDszBl9DYylufxh24zCTc2trb++KsTHsPwKD/+Md/7zmwMRnyoS/8Of3u82ZGlI38XJ7sPsVJKqdoSkS8C1nv/uoj8lYi87L1/3MJLdYJo8m326TFUdaNvDtdHNVZIytk0h+pnGNTejHUhHV029PDCq5AYsgLGW6QA49zwduKHA63L2SchSeRpUjC/2OSD69vcuHkvDB82AIZIPK0Ifu58yt2sx+Yeg4oZJ4Y77YLrmx2WlhosXEjZpYmnBcZSbRstptoJMGwl7wVwbmwfy2qTveGrUi318LMeDkU+at7TEZ9+Kvq7ePJMdh9ipZR6CBF5U0TeFZE/mfZa1LGsAm+VH78NfGF6S1HHNZp8A1ZE5OVpr0k9GT2G9aHnRXW6VNOjOTLJMCyOGbYVjd9z/E6hUUWwCEkUhVml3pc7CAlGLNbYsC00QgREeBriWVk2EHX5aP0O2X4v7JBKqIqZs/DSoueLn5/jt19v0Gr0SSJDbA3GGLqF5eZ2xvqNHZL8Lstze4gNbVheDJ5y228J/8JQZMLuqsZWz3aQdIJyJs/oKNMq1zT+Co0kbvyg6OZZzihSJ4NW2qiZoO8ozj6t0qiFFsN55HvAL05xLer4Vrk/+aa/i7NlFT2GM0/Pi6qOrly5svWlL/3e9Nrc3lqbyLf5EbD2Hz996G3efhf4xkQebhIm2uKmTpZTkbTRgH+26UVNbayiQcas6wDN8uN5tFpzVmnybfbpMayHVfS8qGpGW2vUtNQ55q/9BbeWENfCKtqSUQeHg4znp7gWdTzvMfz9exW4Or2lqKegybfZp8ewHvS8WAPa4qbU9NU95j8NJ/lVNOCfdXpRUw8aZMy+NeD3ReQvgN8F/mm6y1HHpMm32afHsB70vDjj6h4oKjVDVqlxzH8aTg4a8M8+vaipBw0yZpz3fo9wUvwB8Bve+93prkgd0xqafJt1a+gxrAM9L86+VWocKJ4mWjE182od85+G4FcD/tmnFzX1sIYGGTPPe7/jvX/Le78x7bWo49Hk2+zTY1gba+h5cdbVOlA8LbRiqhZqHfPX6sk8gAb8s28NvaiZeRpkKHVyaPJt9ukxnH16XqyFWgeKp8gqWjE162od85+G3aPWgHdEZAX4HeDXp7sc9aS893sisgr8JvA1vaiZXd77HYYnRaWUUupU0/PizKsCxR8QAsUPp7scdUy6I9/sW6PGMX/tkzYa8NeDXtQopZRSSqkTZo0aB4qniFZMzbi6x/zivZ/2GpRSSimllFJq5ojIGUKg+D1tV5xNIvIHwAXv/ddF5E+BD733fz/tdSlV0aSNUkoppZRSSqlTSUQWgXeA71JWTNWtUkPNNk3aKKWUUkoppZQ6tbRiSp1kmrRRSimllFJKKaWUOoF0yJJSSimllFJKKaXUCaRJG6WUUkoppZRSSqkTSJM2SimllFJKKaWUUieQJm2UUkoppZRSSimlTiBN2iillFJKKaWUUkqdQJq0UUoppZRSSimllDqB/h9v31fNRwbpOQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x288 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,4))\n",
    "\n",
    "for i in range(20):\n",
    "    plt.subplot(2,10,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    \n",
    "    # 显示图片\n",
    "    images = plt.imread(train_image_paths[i])\n",
    "    plt.imshow(images)\n",
    "    # 显示标签\n",
    "    plt.xlabel(train_image_label[i])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 配置数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **shuffle()** ： 打乱数据，关于此函数的详细介绍可以参考：https://zhuanlan.zhihu.com/p/42417456\n",
    "- **prefetch()** ：预取数据，加速运行，其详细介绍可以参考我前两篇文章，里面都有讲解。\n",
    "- **cache()** ：将数据集缓存到内存当中，加速运行"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我的电脑GPU配置是 `NVIDIA GeForce RTX 3080` ,`BATCH_SIZE = 8`显存就会报OOM显存不足的错误，在`BATCH_SIZE = 6`时运行正常，每一个epoch时间约为130秒，大家请根据自己的电脑配置动态调整 `BATCH_SIZE`。采用CPU训练的同学，我建议将 `BATCH_SIZE` 调整为2或者1。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<PrefetchDataset shapes: ((None, 299, 299, 3), (None,)), types: (tf.float32, tf.int32)>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BATCH_SIZE = 6\n",
    "\n",
    "# 将训练数据集拆分成训练集与验证集\n",
    "train_ds = image_label_ds.take(5000).shuffle(1000)  # 前1500个batch\n",
    "val_ds   = image_label_ds.skip(5000).shuffle(1000)  # 跳过前1500，选取后面的\n",
    "\n",
    "train_ds = train_ds.batch(BATCH_SIZE)\n",
    "train_ds = train_ds.prefetch(buffer_size=AUTOTUNE)\n",
    "\n",
    "val_ds = val_ds.batch(BATCH_SIZE)\n",
    "val_ds = val_ds.prefetch(buffer_size=AUTOTUNE)\n",
    "val_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6, 299, 299, 3)\n",
      "(6,)\n"
     ]
    }
   ],
   "source": [
    "# 查看数据 shape 进行检查\n",
    "for image_batch, labels_batch in train_ds:\n",
    "    print(image_batch.shape)\n",
    "    print(labels_batch.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 再次查看数据，确认是否被打乱\n",
    "plt.figure(figsize=(8,8))\n",
    "\n",
    "for images, labels in train_ds.take(1):\n",
    "    for i in range(6):\n",
    "        \n",
    "        ax = plt.subplot(4, 3, i + 1)  \n",
    "        plt.imshow(images[i])\n",
    "        plt.title(labels[i].numpy())  #使用.numpy()将张量转换为 NumPy 数组\n",
    "        \n",
    "        plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 三、Inception-ResNet-v2介绍"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "关于Inception系列的介绍可以见：https://baike.baidu.com/item/Inception%E7%BB%93%E6%9E%84 ，个人认为这些在现阶段只需要将模型走一遍（学会搭建），后期如果需要的话，可以再回头来进行详细研究。\n",
    "\n",
    "这个模型相比之前写过的一些模型可能较为复杂一些，先放一张图整体感受一下它"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](pictures/14-1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "关于上面卷积的计算还比较蒙的同学可以参考我这篇文章哈：[卷积的计算](https://blog.csdn.net/qq_38251616/article/details/114278995)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 四、构建Inception-ResNet-v2网络"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.自己搭建"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面是本文的重点 `InceptionResNetV2` 网络模型的构建，可以试着按照上面的图自己构建一下 `InceptionResNetV2`，这部分我主要是参考官网的构建过程，将其单独拎了出来。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"inception_resnet_v2\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 299, 299, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 149, 149, 32) 864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 149, 149, 32) 96          conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 149, 149, 32) 0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 147, 147, 32) 9216        activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 147, 147, 32) 96          conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 147, 147, 32) 0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 147, 147, 64) 18432       activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 147, 147, 64) 192         conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 147, 147, 64) 0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D)    (None, 73, 73, 64)   0           activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 73, 73, 80)   5120        max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 73, 73, 80)   240         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 73, 73, 80)   0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 71, 71, 192)  138240      activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 71, 71, 192)  576         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 71, 71, 192)  0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 35, 35, 192)  0           activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 35, 35, 64)   12288       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 35, 35, 64)   192         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 35, 35, 64)   0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 35, 35, 48)   9216        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 35, 35, 96)   55296       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 35, 35, 48)   144         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 35, 35, 96)   288         conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 35, 35, 48)   0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 35, 35, 96)   0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d (AveragePooli (None, 35, 35, 192)  0           max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 35, 35, 96)   18432       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 35, 35, 64)   76800       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 35, 35, 96)   82944       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 35, 35, 64)   12288       average_pooling2d[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 35, 35, 96)   288         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 35, 35, 64)   192         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 35, 35, 96)   288         conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 35, 35, 64)   192         conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 35, 35, 96)   0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 35, 35, 64)   0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 35, 35, 96)   0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 35, 35, 64)   0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed_5b (Concatenate)          (None, 35, 35, 320)  0           activation_5[0][0]               \n",
      "                                                                 activation_7[0][0]               \n",
      "                                                                 activation_10[0][0]              \n",
      "                                                                 activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 35, 35, 32)   10240       mixed_5b[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 35, 35, 32)   96          conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 35, 35, 32)   0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 35, 35, 32)   10240       mixed_5b[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 35, 35, 48)   13824       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 35, 35, 32)   96          conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 35, 35, 48)   144         conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 35, 35, 32)   0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 35, 35, 48)   0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 35, 35, 32)   10240       mixed_5b[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 35, 35, 32)   9216        activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 35, 35, 64)   27648       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 35, 35, 32)   96          conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 35, 35, 32)   96          conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 35, 35, 64)   192         conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 35, 35, 32)   0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 35, 35, 32)   0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 35, 35, 64)   0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block35_1_mixed (Concatenate)   (None, 35, 35, 128)  0           activation_12[0][0]              \n",
      "                                                                 activation_14[0][0]              \n",
      "                                                                 activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block35_1_conv (Conv2D)         (None, 35, 35, 320)  41280       block35_1_mixed[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block35_1 (Lambda)              (None, 35, 35, 320)  0           mixed_5b[0][0]                   \n",
      "                                                                 block35_1_conv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block35_1_ac (Activation)       (None, 35, 35, 320)  0           block35_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 35, 35, 32)   10240       block35_1_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 35, 35, 32)   96          conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 35, 35, 32)   0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 35, 35, 32)   10240       block35_1_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 35, 35, 48)   13824       activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 35, 35, 32)   96          conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 35, 35, 48)   144         conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 35, 35, 32)   0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 35, 35, 48)   0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 35, 35, 32)   10240       block35_1_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 35, 35, 32)   9216        activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 35, 35, 64)   27648       activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 35, 35, 32)   96          conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 35, 35, 32)   96          conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 35, 35, 64)   192         conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 35, 35, 32)   0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 35, 35, 32)   0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 35, 35, 64)   0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block35_2_mixed (Concatenate)   (None, 35, 35, 128)  0           activation_18[0][0]              \n",
      "                                                                 activation_20[0][0]              \n",
      "                                                                 activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block35_2_conv (Conv2D)         (None, 35, 35, 320)  41280       block35_2_mixed[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block35_2 (Lambda)              (None, 35, 35, 320)  0           block35_1_ac[0][0]               \n",
      "                                                                 block35_2_conv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block35_2_ac (Activation)       (None, 35, 35, 320)  0           block35_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, 35, 35, 32)   10240       block35_2_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, 35, 35, 32)   96          conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 35, 35, 32)   0           batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 35, 35, 32)   10240       block35_2_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, 35, 35, 48)   13824       activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 35, 35, 32)   96          conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, 35, 35, 48)   144         conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 35, 35, 32)   0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 35, 35, 48)   0           batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 35, 35, 32)   10240       block35_2_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, 35, 35, 32)   9216        activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, 35, 35, 64)   27648       activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, 35, 35, 32)   96          conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, 35, 35, 32)   96          conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, 35, 35, 64)   192         conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 35, 35, 32)   0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 35, 35, 32)   0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 35, 35, 64)   0           batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block35_3_mixed (Concatenate)   (None, 35, 35, 128)  0           activation_24[0][0]              \n",
      "                                                                 activation_26[0][0]              \n",
      "                                                                 activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block35_3_conv (Conv2D)         (None, 35, 35, 320)  41280       block35_3_mixed[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block35_3 (Lambda)              (None, 35, 35, 320)  0           block35_2_ac[0][0]               \n",
      "                                                                 block35_3_conv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block35_3_ac (Activation)       (None, 35, 35, 320)  0           block35_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, 35, 35, 32)   10240       block35_3_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_33 (BatchNo (None, 35, 35, 32)   96          conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 35, 35, 32)   0           batch_normalization_33[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, 35, 35, 32)   10240       block35_3_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)              (None, 35, 35, 48)   13824       activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNo (None, 35, 35, 32)   96          conv2d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_34 (BatchNo (None, 35, 35, 48)   144         conv2d_34[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 35, 35, 32)   0           batch_normalization_31[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 35, 35, 48)   0           batch_normalization_34[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, 35, 35, 32)   10240       block35_3_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, 35, 35, 32)   9216        activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)              (None, 35, 35, 64)   27648       activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNo (None, 35, 35, 32)   96          conv2d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_32 (BatchNo (None, 35, 35, 32)   96          conv2d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_35 (BatchNo (None, 35, 35, 64)   192         conv2d_35[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 35, 35, 32)   0           batch_normalization_30[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 35, 35, 32)   0           batch_normalization_32[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 35, 35, 64)   0           batch_normalization_35[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block35_4_mixed (Concatenate)   (None, 35, 35, 128)  0           activation_30[0][0]              \n",
      "                                                                 activation_32[0][0]              \n",
      "                                                                 activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block35_4_conv (Conv2D)         (None, 35, 35, 320)  41280       block35_4_mixed[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block35_4 (Lambda)              (None, 35, 35, 320)  0           block35_3_ac[0][0]               \n",
      "                                                                 block35_4_conv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block35_4_ac (Activation)       (None, 35, 35, 320)  0           block35_4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_39 (Conv2D)              (None, 35, 35, 32)   10240       block35_4_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_39 (BatchNo (None, 35, 35, 32)   96          conv2d_39[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 35, 35, 32)   0           batch_normalization_39[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_37 (Conv2D)              (None, 35, 35, 32)   10240       block35_4_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_40 (Conv2D)              (None, 35, 35, 48)   13824       activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_37 (BatchNo (None, 35, 35, 32)   96          conv2d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_40 (BatchNo (None, 35, 35, 48)   144         conv2d_40[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 35, 35, 32)   0           batch_normalization_37[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 35, 35, 48)   0           batch_normalization_40[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_36 (Conv2D)              (None, 35, 35, 32)   10240       block35_4_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_38 (Conv2D)              (None, 35, 35, 32)   9216        activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_41 (Conv2D)              (None, 35, 35, 64)   27648       activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_36 (BatchNo (None, 35, 35, 32)   96          conv2d_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_38 (BatchNo (None, 35, 35, 32)   96          conv2d_38[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_41 (BatchNo (None, 35, 35, 64)   192         conv2d_41[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 35, 35, 32)   0           batch_normalization_36[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 35, 35, 32)   0           batch_normalization_38[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 35, 35, 64)   0           batch_normalization_41[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block35_5_mixed (Concatenate)   (None, 35, 35, 128)  0           activation_36[0][0]              \n",
      "                                                                 activation_38[0][0]              \n",
      "                                                                 activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block35_5_conv (Conv2D)         (None, 35, 35, 320)  41280       block35_5_mixed[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block35_5 (Lambda)              (None, 35, 35, 320)  0           block35_4_ac[0][0]               \n",
      "                                                                 block35_5_conv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block35_5_ac (Activation)       (None, 35, 35, 320)  0           block35_5[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_45 (Conv2D)              (None, 35, 35, 32)   10240       block35_5_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_45 (BatchNo (None, 35, 35, 32)   96          conv2d_45[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 35, 35, 32)   0           batch_normalization_45[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_43 (Conv2D)              (None, 35, 35, 32)   10240       block35_5_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_46 (Conv2D)              (None, 35, 35, 48)   13824       activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_43 (BatchNo (None, 35, 35, 32)   96          conv2d_43[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_46 (BatchNo (None, 35, 35, 48)   144         conv2d_46[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 35, 35, 32)   0           batch_normalization_43[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 35, 35, 48)   0           batch_normalization_46[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_42 (Conv2D)              (None, 35, 35, 32)   10240       block35_5_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_44 (Conv2D)              (None, 35, 35, 32)   9216        activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_47 (Conv2D)              (None, 35, 35, 64)   27648       activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_42 (BatchNo (None, 35, 35, 32)   96          conv2d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_44 (BatchNo (None, 35, 35, 32)   96          conv2d_44[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_47 (BatchNo (None, 35, 35, 64)   192         conv2d_47[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 35, 35, 32)   0           batch_normalization_42[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 35, 35, 32)   0           batch_normalization_44[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 35, 35, 64)   0           batch_normalization_47[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block35_6_mixed (Concatenate)   (None, 35, 35, 128)  0           activation_42[0][0]              \n",
      "                                                                 activation_44[0][0]              \n",
      "                                                                 activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block35_6_conv (Conv2D)         (None, 35, 35, 320)  41280       block35_6_mixed[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block35_6 (Lambda)              (None, 35, 35, 320)  0           block35_5_ac[0][0]               \n",
      "                                                                 block35_6_conv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block35_6_ac (Activation)       (None, 35, 35, 320)  0           block35_6[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_51 (Conv2D)              (None, 35, 35, 32)   10240       block35_6_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_51 (BatchNo (None, 35, 35, 32)   96          conv2d_51[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_51 (Activation)      (None, 35, 35, 32)   0           batch_normalization_51[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_49 (Conv2D)              (None, 35, 35, 32)   10240       block35_6_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_52 (Conv2D)              (None, 35, 35, 48)   13824       activation_51[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_49 (BatchNo (None, 35, 35, 32)   96          conv2d_49[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_52 (BatchNo (None, 35, 35, 48)   144         conv2d_52[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, 35, 35, 32)   0           batch_normalization_49[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_52 (Activation)      (None, 35, 35, 48)   0           batch_normalization_52[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_48 (Conv2D)              (None, 35, 35, 32)   10240       block35_6_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_50 (Conv2D)              (None, 35, 35, 32)   9216        activation_49[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_53 (Conv2D)              (None, 35, 35, 64)   27648       activation_52[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_48 (BatchNo (None, 35, 35, 32)   96          conv2d_48[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_50 (BatchNo (None, 35, 35, 32)   96          conv2d_50[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_53 (BatchNo (None, 35, 35, 64)   192         conv2d_53[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 35, 35, 32)   0           batch_normalization_48[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_50 (Activation)      (None, 35, 35, 32)   0           batch_normalization_50[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_53 (Activation)      (None, 35, 35, 64)   0           batch_normalization_53[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block35_7_mixed (Concatenate)   (None, 35, 35, 128)  0           activation_48[0][0]              \n",
      "                                                                 activation_50[0][0]              \n",
      "                                                                 activation_53[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block35_7_conv (Conv2D)         (None, 35, 35, 320)  41280       block35_7_mixed[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block35_7 (Lambda)              (None, 35, 35, 320)  0           block35_6_ac[0][0]               \n",
      "                                                                 block35_7_conv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block35_7_ac (Activation)       (None, 35, 35, 320)  0           block35_7[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_57 (Conv2D)              (None, 35, 35, 32)   10240       block35_7_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_57 (BatchNo (None, 35, 35, 32)   96          conv2d_57[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_57 (Activation)      (None, 35, 35, 32)   0           batch_normalization_57[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_55 (Conv2D)              (None, 35, 35, 32)   10240       block35_7_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_58 (Conv2D)              (None, 35, 35, 48)   13824       activation_57[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_55 (BatchNo (None, 35, 35, 32)   96          conv2d_55[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_58 (BatchNo (None, 35, 35, 48)   144         conv2d_58[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_55 (Activation)      (None, 35, 35, 32)   0           batch_normalization_55[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_58 (Activation)      (None, 35, 35, 48)   0           batch_normalization_58[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_54 (Conv2D)              (None, 35, 35, 32)   10240       block35_7_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_56 (Conv2D)              (None, 35, 35, 32)   9216        activation_55[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_59 (Conv2D)              (None, 35, 35, 64)   27648       activation_58[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_54 (BatchNo (None, 35, 35, 32)   96          conv2d_54[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_56 (BatchNo (None, 35, 35, 32)   96          conv2d_56[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_59 (BatchNo (None, 35, 35, 64)   192         conv2d_59[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_54 (Activation)      (None, 35, 35, 32)   0           batch_normalization_54[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_56 (Activation)      (None, 35, 35, 32)   0           batch_normalization_56[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_59 (Activation)      (None, 35, 35, 64)   0           batch_normalization_59[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block35_8_mixed (Concatenate)   (None, 35, 35, 128)  0           activation_54[0][0]              \n",
      "                                                                 activation_56[0][0]              \n",
      "                                                                 activation_59[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block35_8_conv (Conv2D)         (None, 35, 35, 320)  41280       block35_8_mixed[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block35_8 (Lambda)              (None, 35, 35, 320)  0           block35_7_ac[0][0]               \n",
      "                                                                 block35_8_conv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block35_8_ac (Activation)       (None, 35, 35, 320)  0           block35_8[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_63 (Conv2D)              (None, 35, 35, 32)   10240       block35_8_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_63 (BatchNo (None, 35, 35, 32)   96          conv2d_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_63 (Activation)      (None, 35, 35, 32)   0           batch_normalization_63[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_61 (Conv2D)              (None, 35, 35, 32)   10240       block35_8_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_64 (Conv2D)              (None, 35, 35, 48)   13824       activation_63[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_61 (BatchNo (None, 35, 35, 32)   96          conv2d_61[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_64 (BatchNo (None, 35, 35, 48)   144         conv2d_64[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_61 (Activation)      (None, 35, 35, 32)   0           batch_normalization_61[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_64 (Activation)      (None, 35, 35, 48)   0           batch_normalization_64[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_60 (Conv2D)              (None, 35, 35, 32)   10240       block35_8_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_62 (Conv2D)              (None, 35, 35, 32)   9216        activation_61[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_65 (Conv2D)              (None, 35, 35, 64)   27648       activation_64[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_60 (BatchNo (None, 35, 35, 32)   96          conv2d_60[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_62 (BatchNo (None, 35, 35, 32)   96          conv2d_62[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_65 (BatchNo (None, 35, 35, 64)   192         conv2d_65[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_60 (Activation)      (None, 35, 35, 32)   0           batch_normalization_60[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_62 (Activation)      (None, 35, 35, 32)   0           batch_normalization_62[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_65 (Activation)      (None, 35, 35, 64)   0           batch_normalization_65[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block35_9_mixed (Concatenate)   (None, 35, 35, 128)  0           activation_60[0][0]              \n",
      "                                                                 activation_62[0][0]              \n",
      "                                                                 activation_65[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block35_9_conv (Conv2D)         (None, 35, 35, 320)  41280       block35_9_mixed[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block35_9 (Lambda)              (None, 35, 35, 320)  0           block35_8_ac[0][0]               \n",
      "                                                                 block35_9_conv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block35_9_ac (Activation)       (None, 35, 35, 320)  0           block35_9[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_69 (Conv2D)              (None, 35, 35, 32)   10240       block35_9_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_69 (BatchNo (None, 35, 35, 32)   96          conv2d_69[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_69 (Activation)      (None, 35, 35, 32)   0           batch_normalization_69[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_67 (Conv2D)              (None, 35, 35, 32)   10240       block35_9_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_70 (Conv2D)              (None, 35, 35, 48)   13824       activation_69[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_67 (BatchNo (None, 35, 35, 32)   96          conv2d_67[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_70 (BatchNo (None, 35, 35, 48)   144         conv2d_70[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_67 (Activation)      (None, 35, 35, 32)   0           batch_normalization_67[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_70 (Activation)      (None, 35, 35, 48)   0           batch_normalization_70[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_66 (Conv2D)              (None, 35, 35, 32)   10240       block35_9_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_68 (Conv2D)              (None, 35, 35, 32)   9216        activation_67[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_71 (Conv2D)              (None, 35, 35, 64)   27648       activation_70[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_66 (BatchNo (None, 35, 35, 32)   96          conv2d_66[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_68 (BatchNo (None, 35, 35, 32)   96          conv2d_68[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_71 (BatchNo (None, 35, 35, 64)   192         conv2d_71[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_66 (Activation)      (None, 35, 35, 32)   0           batch_normalization_66[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_68 (Activation)      (None, 35, 35, 32)   0           batch_normalization_68[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_71 (Activation)      (None, 35, 35, 64)   0           batch_normalization_71[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block35_10_mixed (Concatenate)  (None, 35, 35, 128)  0           activation_66[0][0]              \n",
      "                                                                 activation_68[0][0]              \n",
      "                                                                 activation_71[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block35_10_conv (Conv2D)        (None, 35, 35, 320)  41280       block35_10_mixed[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block35_10 (Lambda)             (None, 35, 35, 320)  0           block35_9_ac[0][0]               \n",
      "                                                                 block35_10_conv[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block35_10_ac (Activation)      (None, 35, 35, 320)  0           block35_10[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_73 (Conv2D)              (None, 35, 35, 256)  81920       block35_10_ac[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_73 (BatchNo (None, 35, 35, 256)  768         conv2d_73[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_73 (Activation)      (None, 35, 35, 256)  0           batch_normalization_73[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_74 (Conv2D)              (None, 35, 35, 256)  589824      activation_73[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_74 (BatchNo (None, 35, 35, 256)  768         conv2d_74[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_74 (Activation)      (None, 35, 35, 256)  0           batch_normalization_74[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_72 (Conv2D)              (None, 17, 17, 384)  1105920     block35_10_ac[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_75 (Conv2D)              (None, 17, 17, 384)  884736      activation_74[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_72 (BatchNo (None, 17, 17, 384)  1152        conv2d_72[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_75 (BatchNo (None, 17, 17, 384)  1152        conv2d_75[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_72 (Activation)      (None, 17, 17, 384)  0           batch_normalization_72[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_75 (Activation)      (None, 17, 17, 384)  0           batch_normalization_75[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 17, 17, 320)  0           block35_10_ac[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "mixed_6a (Concatenate)          (None, 17, 17, 1088) 0           activation_72[0][0]              \n",
      "                                                                 activation_75[0][0]              \n",
      "                                                                 max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_77 (Conv2D)              (None, 17, 17, 128)  139264      mixed_6a[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_77 (BatchNo (None, 17, 17, 128)  384         conv2d_77[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_77 (Activation)      (None, 17, 17, 128)  0           batch_normalization_77[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_78 (Conv2D)              (None, 17, 17, 160)  143360      activation_77[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_78 (BatchNo (None, 17, 17, 160)  480         conv2d_78[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_78 (Activation)      (None, 17, 17, 160)  0           batch_normalization_78[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_76 (Conv2D)              (None, 17, 17, 192)  208896      mixed_6a[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_79 (Conv2D)              (None, 17, 17, 192)  215040      activation_78[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_76 (BatchNo (None, 17, 17, 192)  576         conv2d_76[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_79 (BatchNo (None, 17, 17, 192)  576         conv2d_79[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_76 (Activation)      (None, 17, 17, 192)  0           batch_normalization_76[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_79 (Activation)      (None, 17, 17, 192)  0           batch_normalization_79[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block17_1_mixed (Concatenate)   (None, 17, 17, 384)  0           activation_76[0][0]              \n",
      "                                                                 activation_79[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block17_1_conv (Conv2D)         (None, 17, 17, 1088) 418880      block17_1_mixed[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block17_1 (Lambda)              (None, 17, 17, 1088) 0           mixed_6a[0][0]                   \n",
      "                                                                 block17_1_conv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block17_1_ac (Activation)       (None, 17, 17, 1088) 0           block17_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_81 (Conv2D)              (None, 17, 17, 128)  139264      block17_1_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_81 (BatchNo (None, 17, 17, 128)  384         conv2d_81[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_81 (Activation)      (None, 17, 17, 128)  0           batch_normalization_81[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_82 (Conv2D)              (None, 17, 17, 160)  143360      activation_81[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_82 (BatchNo (None, 17, 17, 160)  480         conv2d_82[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_82 (Activation)      (None, 17, 17, 160)  0           batch_normalization_82[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_80 (Conv2D)              (None, 17, 17, 192)  208896      block17_1_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_83 (Conv2D)              (None, 17, 17, 192)  215040      activation_82[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_80 (BatchNo (None, 17, 17, 192)  576         conv2d_80[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_83 (BatchNo (None, 17, 17, 192)  576         conv2d_83[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_80 (Activation)      (None, 17, 17, 192)  0           batch_normalization_80[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_83 (Activation)      (None, 17, 17, 192)  0           batch_normalization_83[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block17_2_mixed (Concatenate)   (None, 17, 17, 384)  0           activation_80[0][0]              \n",
      "                                                                 activation_83[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block17_2_conv (Conv2D)         (None, 17, 17, 1088) 418880      block17_2_mixed[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block17_2 (Lambda)              (None, 17, 17, 1088) 0           block17_1_ac[0][0]               \n",
      "                                                                 block17_2_conv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block17_2_ac (Activation)       (None, 17, 17, 1088) 0           block17_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_85 (Conv2D)              (None, 17, 17, 128)  139264      block17_2_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_85 (BatchNo (None, 17, 17, 128)  384         conv2d_85[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_85 (Activation)      (None, 17, 17, 128)  0           batch_normalization_85[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_86 (Conv2D)              (None, 17, 17, 160)  143360      activation_85[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_86 (BatchNo (None, 17, 17, 160)  480         conv2d_86[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_86 (Activation)      (None, 17, 17, 160)  0           batch_normalization_86[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_84 (Conv2D)              (None, 17, 17, 192)  208896      block17_2_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_87 (Conv2D)              (None, 17, 17, 192)  215040      activation_86[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_84 (BatchNo (None, 17, 17, 192)  576         conv2d_84[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_87 (BatchNo (None, 17, 17, 192)  576         conv2d_87[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_84 (Activation)      (None, 17, 17, 192)  0           batch_normalization_84[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_87 (Activation)      (None, 17, 17, 192)  0           batch_normalization_87[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block17_3_mixed (Concatenate)   (None, 17, 17, 384)  0           activation_84[0][0]              \n",
      "                                                                 activation_87[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block17_3_conv (Conv2D)         (None, 17, 17, 1088) 418880      block17_3_mixed[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block17_3 (Lambda)              (None, 17, 17, 1088) 0           block17_2_ac[0][0]               \n",
      "                                                                 block17_3_conv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block17_3_ac (Activation)       (None, 17, 17, 1088) 0           block17_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_89 (Conv2D)              (None, 17, 17, 128)  139264      block17_3_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_89 (BatchNo (None, 17, 17, 128)  384         conv2d_89[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_89 (Activation)      (None, 17, 17, 128)  0           batch_normalization_89[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_90 (Conv2D)              (None, 17, 17, 160)  143360      activation_89[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_90 (BatchNo (None, 17, 17, 160)  480         conv2d_90[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_90 (Activation)      (None, 17, 17, 160)  0           batch_normalization_90[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_88 (Conv2D)              (None, 17, 17, 192)  208896      block17_3_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_91 (Conv2D)              (None, 17, 17, 192)  215040      activation_90[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_88 (BatchNo (None, 17, 17, 192)  576         conv2d_88[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_91 (BatchNo (None, 17, 17, 192)  576         conv2d_91[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_88 (Activation)      (None, 17, 17, 192)  0           batch_normalization_88[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_91 (Activation)      (None, 17, 17, 192)  0           batch_normalization_91[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block17_4_mixed (Concatenate)   (None, 17, 17, 384)  0           activation_88[0][0]              \n",
      "                                                                 activation_91[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block17_4_conv (Conv2D)         (None, 17, 17, 1088) 418880      block17_4_mixed[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block17_4 (Lambda)              (None, 17, 17, 1088) 0           block17_3_ac[0][0]               \n",
      "                                                                 block17_4_conv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block17_4_ac (Activation)       (None, 17, 17, 1088) 0           block17_4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_93 (Conv2D)              (None, 17, 17, 128)  139264      block17_4_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_93 (BatchNo (None, 17, 17, 128)  384         conv2d_93[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_93 (Activation)      (None, 17, 17, 128)  0           batch_normalization_93[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_94 (Conv2D)              (None, 17, 17, 160)  143360      activation_93[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_94 (BatchNo (None, 17, 17, 160)  480         conv2d_94[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_94 (Activation)      (None, 17, 17, 160)  0           batch_normalization_94[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_92 (Conv2D)              (None, 17, 17, 192)  208896      block17_4_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_95 (Conv2D)              (None, 17, 17, 192)  215040      activation_94[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_92 (BatchNo (None, 17, 17, 192)  576         conv2d_92[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_95 (BatchNo (None, 17, 17, 192)  576         conv2d_95[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_92 (Activation)      (None, 17, 17, 192)  0           batch_normalization_92[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_95 (Activation)      (None, 17, 17, 192)  0           batch_normalization_95[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block17_5_mixed (Concatenate)   (None, 17, 17, 384)  0           activation_92[0][0]              \n",
      "                                                                 activation_95[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block17_5_conv (Conv2D)         (None, 17, 17, 1088) 418880      block17_5_mixed[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block17_5 (Lambda)              (None, 17, 17, 1088) 0           block17_4_ac[0][0]               \n",
      "                                                                 block17_5_conv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block17_5_ac (Activation)       (None, 17, 17, 1088) 0           block17_5[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_97 (Conv2D)              (None, 17, 17, 128)  139264      block17_5_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_97 (BatchNo (None, 17, 17, 128)  384         conv2d_97[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_97 (Activation)      (None, 17, 17, 128)  0           batch_normalization_97[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_98 (Conv2D)              (None, 17, 17, 160)  143360      activation_97[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_98 (BatchNo (None, 17, 17, 160)  480         conv2d_98[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_98 (Activation)      (None, 17, 17, 160)  0           batch_normalization_98[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_96 (Conv2D)              (None, 17, 17, 192)  208896      block17_5_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_99 (Conv2D)              (None, 17, 17, 192)  215040      activation_98[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_96 (BatchNo (None, 17, 17, 192)  576         conv2d_96[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_99 (BatchNo (None, 17, 17, 192)  576         conv2d_99[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_96 (Activation)      (None, 17, 17, 192)  0           batch_normalization_96[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_99 (Activation)      (None, 17, 17, 192)  0           batch_normalization_99[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block17_6_mixed (Concatenate)   (None, 17, 17, 384)  0           activation_96[0][0]              \n",
      "                                                                 activation_99[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block17_6_conv (Conv2D)         (None, 17, 17, 1088) 418880      block17_6_mixed[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block17_6 (Lambda)              (None, 17, 17, 1088) 0           block17_5_ac[0][0]               \n",
      "                                                                 block17_6_conv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block17_6_ac (Activation)       (None, 17, 17, 1088) 0           block17_6[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_101 (Conv2D)             (None, 17, 17, 128)  139264      block17_6_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_101 (BatchN (None, 17, 17, 128)  384         conv2d_101[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_101 (Activation)     (None, 17, 17, 128)  0           batch_normalization_101[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_102 (Conv2D)             (None, 17, 17, 160)  143360      activation_101[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_102 (BatchN (None, 17, 17, 160)  480         conv2d_102[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_102 (Activation)     (None, 17, 17, 160)  0           batch_normalization_102[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_100 (Conv2D)             (None, 17, 17, 192)  208896      block17_6_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_103 (Conv2D)             (None, 17, 17, 192)  215040      activation_102[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_100 (BatchN (None, 17, 17, 192)  576         conv2d_100[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_103 (BatchN (None, 17, 17, 192)  576         conv2d_103[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_100 (Activation)     (None, 17, 17, 192)  0           batch_normalization_100[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_103 (Activation)     (None, 17, 17, 192)  0           batch_normalization_103[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block17_7_mixed (Concatenate)   (None, 17, 17, 384)  0           activation_100[0][0]             \n",
      "                                                                 activation_103[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block17_7_conv (Conv2D)         (None, 17, 17, 1088) 418880      block17_7_mixed[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block17_7 (Lambda)              (None, 17, 17, 1088) 0           block17_6_ac[0][0]               \n",
      "                                                                 block17_7_conv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block17_7_ac (Activation)       (None, 17, 17, 1088) 0           block17_7[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_105 (Conv2D)             (None, 17, 17, 128)  139264      block17_7_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_105 (BatchN (None, 17, 17, 128)  384         conv2d_105[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_105 (Activation)     (None, 17, 17, 128)  0           batch_normalization_105[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_106 (Conv2D)             (None, 17, 17, 160)  143360      activation_105[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_106 (BatchN (None, 17, 17, 160)  480         conv2d_106[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_106 (Activation)     (None, 17, 17, 160)  0           batch_normalization_106[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_104 (Conv2D)             (None, 17, 17, 192)  208896      block17_7_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_107 (Conv2D)             (None, 17, 17, 192)  215040      activation_106[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_104 (BatchN (None, 17, 17, 192)  576         conv2d_104[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_107 (BatchN (None, 17, 17, 192)  576         conv2d_107[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_104 (Activation)     (None, 17, 17, 192)  0           batch_normalization_104[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_107 (Activation)     (None, 17, 17, 192)  0           batch_normalization_107[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block17_8_mixed (Concatenate)   (None, 17, 17, 384)  0           activation_104[0][0]             \n",
      "                                                                 activation_107[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block17_8_conv (Conv2D)         (None, 17, 17, 1088) 418880      block17_8_mixed[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block17_8 (Lambda)              (None, 17, 17, 1088) 0           block17_7_ac[0][0]               \n",
      "                                                                 block17_8_conv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block17_8_ac (Activation)       (None, 17, 17, 1088) 0           block17_8[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_109 (Conv2D)             (None, 17, 17, 128)  139264      block17_8_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_109 (BatchN (None, 17, 17, 128)  384         conv2d_109[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_109 (Activation)     (None, 17, 17, 128)  0           batch_normalization_109[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_110 (Conv2D)             (None, 17, 17, 160)  143360      activation_109[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_110 (BatchN (None, 17, 17, 160)  480         conv2d_110[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_110 (Activation)     (None, 17, 17, 160)  0           batch_normalization_110[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_108 (Conv2D)             (None, 17, 17, 192)  208896      block17_8_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_111 (Conv2D)             (None, 17, 17, 192)  215040      activation_110[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_108 (BatchN (None, 17, 17, 192)  576         conv2d_108[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_111 (BatchN (None, 17, 17, 192)  576         conv2d_111[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_108 (Activation)     (None, 17, 17, 192)  0           batch_normalization_108[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_111 (Activation)     (None, 17, 17, 192)  0           batch_normalization_111[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block17_9_mixed (Concatenate)   (None, 17, 17, 384)  0           activation_108[0][0]             \n",
      "                                                                 activation_111[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block17_9_conv (Conv2D)         (None, 17, 17, 1088) 418880      block17_9_mixed[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block17_9 (Lambda)              (None, 17, 17, 1088) 0           block17_8_ac[0][0]               \n",
      "                                                                 block17_9_conv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block17_9_ac (Activation)       (None, 17, 17, 1088) 0           block17_9[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_113 (Conv2D)             (None, 17, 17, 128)  139264      block17_9_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_113 (BatchN (None, 17, 17, 128)  384         conv2d_113[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_113 (Activation)     (None, 17, 17, 128)  0           batch_normalization_113[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_114 (Conv2D)             (None, 17, 17, 160)  143360      activation_113[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_114 (BatchN (None, 17, 17, 160)  480         conv2d_114[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_114 (Activation)     (None, 17, 17, 160)  0           batch_normalization_114[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_112 (Conv2D)             (None, 17, 17, 192)  208896      block17_9_ac[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_115 (Conv2D)             (None, 17, 17, 192)  215040      activation_114[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_112 (BatchN (None, 17, 17, 192)  576         conv2d_112[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_115 (BatchN (None, 17, 17, 192)  576         conv2d_115[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_112 (Activation)     (None, 17, 17, 192)  0           batch_normalization_112[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_115 (Activation)     (None, 17, 17, 192)  0           batch_normalization_115[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block17_10_mixed (Concatenate)  (None, 17, 17, 384)  0           activation_112[0][0]             \n",
      "                                                                 activation_115[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block17_10_conv (Conv2D)        (None, 17, 17, 1088) 418880      block17_10_mixed[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block17_10 (Lambda)             (None, 17, 17, 1088) 0           block17_9_ac[0][0]               \n",
      "                                                                 block17_10_conv[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block17_10_ac (Activation)      (None, 17, 17, 1088) 0           block17_10[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_117 (Conv2D)             (None, 17, 17, 128)  139264      block17_10_ac[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_117 (BatchN (None, 17, 17, 128)  384         conv2d_117[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_117 (Activation)     (None, 17, 17, 128)  0           batch_normalization_117[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_118 (Conv2D)             (None, 17, 17, 160)  143360      activation_117[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_118 (BatchN (None, 17, 17, 160)  480         conv2d_118[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_118 (Activation)     (None, 17, 17, 160)  0           batch_normalization_118[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_116 (Conv2D)             (None, 17, 17, 192)  208896      block17_10_ac[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_119 (Conv2D)             (None, 17, 17, 192)  215040      activation_118[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_116 (BatchN (None, 17, 17, 192)  576         conv2d_116[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_119 (BatchN (None, 17, 17, 192)  576         conv2d_119[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_116 (Activation)     (None, 17, 17, 192)  0           batch_normalization_116[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_119 (Activation)     (None, 17, 17, 192)  0           batch_normalization_119[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block17_11_mixed (Concatenate)  (None, 17, 17, 384)  0           activation_116[0][0]             \n",
      "                                                                 activation_119[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block17_11_conv (Conv2D)        (None, 17, 17, 1088) 418880      block17_11_mixed[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block17_11 (Lambda)             (None, 17, 17, 1088) 0           block17_10_ac[0][0]              \n",
      "                                                                 block17_11_conv[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block17_11_ac (Activation)      (None, 17, 17, 1088) 0           block17_11[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_121 (Conv2D)             (None, 17, 17, 128)  139264      block17_11_ac[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_121 (BatchN (None, 17, 17, 128)  384         conv2d_121[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_121 (Activation)     (None, 17, 17, 128)  0           batch_normalization_121[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_122 (Conv2D)             (None, 17, 17, 160)  143360      activation_121[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_122 (BatchN (None, 17, 17, 160)  480         conv2d_122[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_122 (Activation)     (None, 17, 17, 160)  0           batch_normalization_122[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_120 (Conv2D)             (None, 17, 17, 192)  208896      block17_11_ac[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_123 (Conv2D)             (None, 17, 17, 192)  215040      activation_122[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_120 (BatchN (None, 17, 17, 192)  576         conv2d_120[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_123 (BatchN (None, 17, 17, 192)  576         conv2d_123[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_120 (Activation)     (None, 17, 17, 192)  0           batch_normalization_120[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_123 (Activation)     (None, 17, 17, 192)  0           batch_normalization_123[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block17_12_mixed (Concatenate)  (None, 17, 17, 384)  0           activation_120[0][0]             \n",
      "                                                                 activation_123[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block17_12_conv (Conv2D)        (None, 17, 17, 1088) 418880      block17_12_mixed[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block17_12 (Lambda)             (None, 17, 17, 1088) 0           block17_11_ac[0][0]              \n",
      "                                                                 block17_12_conv[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block17_12_ac (Activation)      (None, 17, 17, 1088) 0           block17_12[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_125 (Conv2D)             (None, 17, 17, 128)  139264      block17_12_ac[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_125 (BatchN (None, 17, 17, 128)  384         conv2d_125[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_125 (Activation)     (None, 17, 17, 128)  0           batch_normalization_125[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_126 (Conv2D)             (None, 17, 17, 160)  143360      activation_125[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_126 (BatchN (None, 17, 17, 160)  480         conv2d_126[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_126 (Activation)     (None, 17, 17, 160)  0           batch_normalization_126[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_124 (Conv2D)             (None, 17, 17, 192)  208896      block17_12_ac[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_127 (Conv2D)             (None, 17, 17, 192)  215040      activation_126[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_124 (BatchN (None, 17, 17, 192)  576         conv2d_124[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_127 (BatchN (None, 17, 17, 192)  576         conv2d_127[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_124 (Activation)     (None, 17, 17, 192)  0           batch_normalization_124[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_127 (Activation)     (None, 17, 17, 192)  0           batch_normalization_127[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block17_13_mixed (Concatenate)  (None, 17, 17, 384)  0           activation_124[0][0]             \n",
      "                                                                 activation_127[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block17_13_conv (Conv2D)        (None, 17, 17, 1088) 418880      block17_13_mixed[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block17_13 (Lambda)             (None, 17, 17, 1088) 0           block17_12_ac[0][0]              \n",
      "                                                                 block17_13_conv[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block17_13_ac (Activation)      (None, 17, 17, 1088) 0           block17_13[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_129 (Conv2D)             (None, 17, 17, 128)  139264      block17_13_ac[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_129 (BatchN (None, 17, 17, 128)  384         conv2d_129[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_129 (Activation)     (None, 17, 17, 128)  0           batch_normalization_129[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_130 (Conv2D)             (None, 17, 17, 160)  143360      activation_129[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_130 (BatchN (None, 17, 17, 160)  480         conv2d_130[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_130 (Activation)     (None, 17, 17, 160)  0           batch_normalization_130[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_128 (Conv2D)             (None, 17, 17, 192)  208896      block17_13_ac[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_131 (Conv2D)             (None, 17, 17, 192)  215040      activation_130[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_128 (BatchN (None, 17, 17, 192)  576         conv2d_128[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_131 (BatchN (None, 17, 17, 192)  576         conv2d_131[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_128 (Activation)     (None, 17, 17, 192)  0           batch_normalization_128[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_131 (Activation)     (None, 17, 17, 192)  0           batch_normalization_131[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block17_14_mixed (Concatenate)  (None, 17, 17, 384)  0           activation_128[0][0]             \n",
      "                                                                 activation_131[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block17_14_conv (Conv2D)        (None, 17, 17, 1088) 418880      block17_14_mixed[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block17_14 (Lambda)             (None, 17, 17, 1088) 0           block17_13_ac[0][0]              \n",
      "                                                                 block17_14_conv[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block17_14_ac (Activation)      (None, 17, 17, 1088) 0           block17_14[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_133 (Conv2D)             (None, 17, 17, 128)  139264      block17_14_ac[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_133 (BatchN (None, 17, 17, 128)  384         conv2d_133[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_133 (Activation)     (None, 17, 17, 128)  0           batch_normalization_133[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_134 (Conv2D)             (None, 17, 17, 160)  143360      activation_133[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_134 (BatchN (None, 17, 17, 160)  480         conv2d_134[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_134 (Activation)     (None, 17, 17, 160)  0           batch_normalization_134[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_132 (Conv2D)             (None, 17, 17, 192)  208896      block17_14_ac[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_135 (Conv2D)             (None, 17, 17, 192)  215040      activation_134[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_132 (BatchN (None, 17, 17, 192)  576         conv2d_132[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_135 (BatchN (None, 17, 17, 192)  576         conv2d_135[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_132 (Activation)     (None, 17, 17, 192)  0           batch_normalization_132[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_135 (Activation)     (None, 17, 17, 192)  0           batch_normalization_135[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block17_15_mixed (Concatenate)  (None, 17, 17, 384)  0           activation_132[0][0]             \n",
      "                                                                 activation_135[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block17_15_conv (Conv2D)        (None, 17, 17, 1088) 418880      block17_15_mixed[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block17_15 (Lambda)             (None, 17, 17, 1088) 0           block17_14_ac[0][0]              \n",
      "                                                                 block17_15_conv[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block17_15_ac (Activation)      (None, 17, 17, 1088) 0           block17_15[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_137 (Conv2D)             (None, 17, 17, 128)  139264      block17_15_ac[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_137 (BatchN (None, 17, 17, 128)  384         conv2d_137[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_137 (Activation)     (None, 17, 17, 128)  0           batch_normalization_137[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_138 (Conv2D)             (None, 17, 17, 160)  143360      activation_137[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_138 (BatchN (None, 17, 17, 160)  480         conv2d_138[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_138 (Activation)     (None, 17, 17, 160)  0           batch_normalization_138[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_136 (Conv2D)             (None, 17, 17, 192)  208896      block17_15_ac[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_139 (Conv2D)             (None, 17, 17, 192)  215040      activation_138[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_136 (BatchN (None, 17, 17, 192)  576         conv2d_136[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_139 (BatchN (None, 17, 17, 192)  576         conv2d_139[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_136 (Activation)     (None, 17, 17, 192)  0           batch_normalization_136[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_139 (Activation)     (None, 17, 17, 192)  0           batch_normalization_139[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block17_16_mixed (Concatenate)  (None, 17, 17, 384)  0           activation_136[0][0]             \n",
      "                                                                 activation_139[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block17_16_conv (Conv2D)        (None, 17, 17, 1088) 418880      block17_16_mixed[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block17_16 (Lambda)             (None, 17, 17, 1088) 0           block17_15_ac[0][0]              \n",
      "                                                                 block17_16_conv[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block17_16_ac (Activation)      (None, 17, 17, 1088) 0           block17_16[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_141 (Conv2D)             (None, 17, 17, 128)  139264      block17_16_ac[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_141 (BatchN (None, 17, 17, 128)  384         conv2d_141[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_141 (Activation)     (None, 17, 17, 128)  0           batch_normalization_141[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_142 (Conv2D)             (None, 17, 17, 160)  143360      activation_141[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_142 (BatchN (None, 17, 17, 160)  480         conv2d_142[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_142 (Activation)     (None, 17, 17, 160)  0           batch_normalization_142[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_140 (Conv2D)             (None, 17, 17, 192)  208896      block17_16_ac[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_143 (Conv2D)             (None, 17, 17, 192)  215040      activation_142[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_140 (BatchN (None, 17, 17, 192)  576         conv2d_140[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_143 (BatchN (None, 17, 17, 192)  576         conv2d_143[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_140 (Activation)     (None, 17, 17, 192)  0           batch_normalization_140[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_143 (Activation)     (None, 17, 17, 192)  0           batch_normalization_143[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block17_17_mixed (Concatenate)  (None, 17, 17, 384)  0           activation_140[0][0]             \n",
      "                                                                 activation_143[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block17_17_conv (Conv2D)        (None, 17, 17, 1088) 418880      block17_17_mixed[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block17_17 (Lambda)             (None, 17, 17, 1088) 0           block17_16_ac[0][0]              \n",
      "                                                                 block17_17_conv[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block17_17_ac (Activation)      (None, 17, 17, 1088) 0           block17_17[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_145 (Conv2D)             (None, 17, 17, 128)  139264      block17_17_ac[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_145 (BatchN (None, 17, 17, 128)  384         conv2d_145[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_145 (Activation)     (None, 17, 17, 128)  0           batch_normalization_145[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_146 (Conv2D)             (None, 17, 17, 160)  143360      activation_145[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_146 (BatchN (None, 17, 17, 160)  480         conv2d_146[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_146 (Activation)     (None, 17, 17, 160)  0           batch_normalization_146[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_144 (Conv2D)             (None, 17, 17, 192)  208896      block17_17_ac[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_147 (Conv2D)             (None, 17, 17, 192)  215040      activation_146[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_144 (BatchN (None, 17, 17, 192)  576         conv2d_144[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_147 (BatchN (None, 17, 17, 192)  576         conv2d_147[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_144 (Activation)     (None, 17, 17, 192)  0           batch_normalization_144[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_147 (Activation)     (None, 17, 17, 192)  0           batch_normalization_147[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block17_18_mixed (Concatenate)  (None, 17, 17, 384)  0           activation_144[0][0]             \n",
      "                                                                 activation_147[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block17_18_conv (Conv2D)        (None, 17, 17, 1088) 418880      block17_18_mixed[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block17_18 (Lambda)             (None, 17, 17, 1088) 0           block17_17_ac[0][0]              \n",
      "                                                                 block17_18_conv[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block17_18_ac (Activation)      (None, 17, 17, 1088) 0           block17_18[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_149 (Conv2D)             (None, 17, 17, 128)  139264      block17_18_ac[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_149 (BatchN (None, 17, 17, 128)  384         conv2d_149[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_149 (Activation)     (None, 17, 17, 128)  0           batch_normalization_149[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_150 (Conv2D)             (None, 17, 17, 160)  143360      activation_149[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_150 (BatchN (None, 17, 17, 160)  480         conv2d_150[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_150 (Activation)     (None, 17, 17, 160)  0           batch_normalization_150[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_148 (Conv2D)             (None, 17, 17, 192)  208896      block17_18_ac[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_151 (Conv2D)             (None, 17, 17, 192)  215040      activation_150[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_148 (BatchN (None, 17, 17, 192)  576         conv2d_148[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_151 (BatchN (None, 17, 17, 192)  576         conv2d_151[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_148 (Activation)     (None, 17, 17, 192)  0           batch_normalization_148[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_151 (Activation)     (None, 17, 17, 192)  0           batch_normalization_151[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block17_19_mixed (Concatenate)  (None, 17, 17, 384)  0           activation_148[0][0]             \n",
      "                                                                 activation_151[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block17_19_conv (Conv2D)        (None, 17, 17, 1088) 418880      block17_19_mixed[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block17_19 (Lambda)             (None, 17, 17, 1088) 0           block17_18_ac[0][0]              \n",
      "                                                                 block17_19_conv[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block17_19_ac (Activation)      (None, 17, 17, 1088) 0           block17_19[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_153 (Conv2D)             (None, 17, 17, 128)  139264      block17_19_ac[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_153 (BatchN (None, 17, 17, 128)  384         conv2d_153[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_153 (Activation)     (None, 17, 17, 128)  0           batch_normalization_153[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_154 (Conv2D)             (None, 17, 17, 160)  143360      activation_153[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_154 (BatchN (None, 17, 17, 160)  480         conv2d_154[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_154 (Activation)     (None, 17, 17, 160)  0           batch_normalization_154[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_152 (Conv2D)             (None, 17, 17, 192)  208896      block17_19_ac[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_155 (Conv2D)             (None, 17, 17, 192)  215040      activation_154[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_152 (BatchN (None, 17, 17, 192)  576         conv2d_152[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_155 (BatchN (None, 17, 17, 192)  576         conv2d_155[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_152 (Activation)     (None, 17, 17, 192)  0           batch_normalization_152[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_155 (Activation)     (None, 17, 17, 192)  0           batch_normalization_155[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block17_20_mixed (Concatenate)  (None, 17, 17, 384)  0           activation_152[0][0]             \n",
      "                                                                 activation_155[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block17_20_conv (Conv2D)        (None, 17, 17, 1088) 418880      block17_20_mixed[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block17_20 (Lambda)             (None, 17, 17, 1088) 0           block17_19_ac[0][0]              \n",
      "                                                                 block17_20_conv[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block17_20_ac (Activation)      (None, 17, 17, 1088) 0           block17_20[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_160 (Conv2D)             (None, 17, 17, 256)  278528      block17_20_ac[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_160 (BatchN (None, 17, 17, 256)  768         conv2d_160[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_160 (Activation)     (None, 17, 17, 256)  0           batch_normalization_160[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_156 (Conv2D)             (None, 17, 17, 256)  278528      block17_20_ac[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_158 (Conv2D)             (None, 17, 17, 256)  278528      block17_20_ac[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_161 (Conv2D)             (None, 17, 17, 288)  663552      activation_160[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_156 (BatchN (None, 17, 17, 256)  768         conv2d_156[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_158 (BatchN (None, 17, 17, 256)  768         conv2d_158[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_161 (BatchN (None, 17, 17, 288)  864         conv2d_161[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_156 (Activation)     (None, 17, 17, 256)  0           batch_normalization_156[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_158 (Activation)     (None, 17, 17, 256)  0           batch_normalization_158[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_161 (Activation)     (None, 17, 17, 288)  0           batch_normalization_161[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_157 (Conv2D)             (None, 8, 8, 384)    884736      activation_156[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_159 (Conv2D)             (None, 8, 8, 288)    663552      activation_158[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_162 (Conv2D)             (None, 8, 8, 320)    829440      activation_161[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_157 (BatchN (None, 8, 8, 384)    1152        conv2d_157[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_159 (BatchN (None, 8, 8, 288)    864         conv2d_159[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_162 (BatchN (None, 8, 8, 320)    960         conv2d_162[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_157 (Activation)     (None, 8, 8, 384)    0           batch_normalization_157[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_159 (Activation)     (None, 8, 8, 288)    0           batch_normalization_159[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_162 (Activation)     (None, 8, 8, 320)    0           batch_normalization_162[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 8, 8, 1088)   0           block17_20_ac[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "mixed_7a (Concatenate)          (None, 8, 8, 2080)   0           activation_157[0][0]             \n",
      "                                                                 activation_159[0][0]             \n",
      "                                                                 activation_162[0][0]             \n",
      "                                                                 max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_164 (Conv2D)             (None, 8, 8, 192)    399360      mixed_7a[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_164 (BatchN (None, 8, 8, 192)    576         conv2d_164[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_164 (Activation)     (None, 8, 8, 192)    0           batch_normalization_164[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_165 (Conv2D)             (None, 8, 8, 224)    129024      activation_164[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_165 (BatchN (None, 8, 8, 224)    672         conv2d_165[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_165 (Activation)     (None, 8, 8, 224)    0           batch_normalization_165[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_163 (Conv2D)             (None, 8, 8, 192)    399360      mixed_7a[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_166 (Conv2D)             (None, 8, 8, 256)    172032      activation_165[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_163 (BatchN (None, 8, 8, 192)    576         conv2d_163[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_166 (BatchN (None, 8, 8, 256)    768         conv2d_166[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_163 (Activation)     (None, 8, 8, 192)    0           batch_normalization_163[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_166 (Activation)     (None, 8, 8, 256)    0           batch_normalization_166[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block8_1_mixed (Concatenate)    (None, 8, 8, 448)    0           activation_163[0][0]             \n",
      "                                                                 activation_166[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block8_1_conv (Conv2D)          (None, 8, 8, 2080)   933920      block8_1_mixed[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block8_1 (Lambda)               (None, 8, 8, 2080)   0           mixed_7a[0][0]                   \n",
      "                                                                 block8_1_conv[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block8_1_ac (Activation)        (None, 8, 8, 2080)   0           block8_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_168 (Conv2D)             (None, 8, 8, 192)    399360      block8_1_ac[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_168 (BatchN (None, 8, 8, 192)    576         conv2d_168[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_168 (Activation)     (None, 8, 8, 192)    0           batch_normalization_168[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_169 (Conv2D)             (None, 8, 8, 224)    129024      activation_168[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_169 (BatchN (None, 8, 8, 224)    672         conv2d_169[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_169 (Activation)     (None, 8, 8, 224)    0           batch_normalization_169[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_167 (Conv2D)             (None, 8, 8, 192)    399360      block8_1_ac[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_170 (Conv2D)             (None, 8, 8, 256)    172032      activation_169[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_167 (BatchN (None, 8, 8, 192)    576         conv2d_167[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_170 (BatchN (None, 8, 8, 256)    768         conv2d_170[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_167 (Activation)     (None, 8, 8, 192)    0           batch_normalization_167[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_170 (Activation)     (None, 8, 8, 256)    0           batch_normalization_170[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block8_2_mixed (Concatenate)    (None, 8, 8, 448)    0           activation_167[0][0]             \n",
      "                                                                 activation_170[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block8_2_conv (Conv2D)          (None, 8, 8, 2080)   933920      block8_2_mixed[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block8_2 (Lambda)               (None, 8, 8, 2080)   0           block8_1_ac[0][0]                \n",
      "                                                                 block8_2_conv[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block8_2_ac (Activation)        (None, 8, 8, 2080)   0           block8_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_172 (Conv2D)             (None, 8, 8, 192)    399360      block8_2_ac[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_172 (BatchN (None, 8, 8, 192)    576         conv2d_172[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_172 (Activation)     (None, 8, 8, 192)    0           batch_normalization_172[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_173 (Conv2D)             (None, 8, 8, 224)    129024      activation_172[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_173 (BatchN (None, 8, 8, 224)    672         conv2d_173[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_173 (Activation)     (None, 8, 8, 224)    0           batch_normalization_173[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_171 (Conv2D)             (None, 8, 8, 192)    399360      block8_2_ac[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_174 (Conv2D)             (None, 8, 8, 256)    172032      activation_173[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_171 (BatchN (None, 8, 8, 192)    576         conv2d_171[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_174 (BatchN (None, 8, 8, 256)    768         conv2d_174[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_171 (Activation)     (None, 8, 8, 192)    0           batch_normalization_171[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_174 (Activation)     (None, 8, 8, 256)    0           batch_normalization_174[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block8_3_mixed (Concatenate)    (None, 8, 8, 448)    0           activation_171[0][0]             \n",
      "                                                                 activation_174[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block8_3_conv (Conv2D)          (None, 8, 8, 2080)   933920      block8_3_mixed[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block8_3 (Lambda)               (None, 8, 8, 2080)   0           block8_2_ac[0][0]                \n",
      "                                                                 block8_3_conv[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block8_3_ac (Activation)        (None, 8, 8, 2080)   0           block8_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_176 (Conv2D)             (None, 8, 8, 192)    399360      block8_3_ac[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_176 (BatchN (None, 8, 8, 192)    576         conv2d_176[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_176 (Activation)     (None, 8, 8, 192)    0           batch_normalization_176[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_177 (Conv2D)             (None, 8, 8, 224)    129024      activation_176[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_177 (BatchN (None, 8, 8, 224)    672         conv2d_177[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_177 (Activation)     (None, 8, 8, 224)    0           batch_normalization_177[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_175 (Conv2D)             (None, 8, 8, 192)    399360      block8_3_ac[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_178 (Conv2D)             (None, 8, 8, 256)    172032      activation_177[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_175 (BatchN (None, 8, 8, 192)    576         conv2d_175[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_178 (BatchN (None, 8, 8, 256)    768         conv2d_178[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_175 (Activation)     (None, 8, 8, 192)    0           batch_normalization_175[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_178 (Activation)     (None, 8, 8, 256)    0           batch_normalization_178[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block8_4_mixed (Concatenate)    (None, 8, 8, 448)    0           activation_175[0][0]             \n",
      "                                                                 activation_178[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block8_4_conv (Conv2D)          (None, 8, 8, 2080)   933920      block8_4_mixed[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block8_4 (Lambda)               (None, 8, 8, 2080)   0           block8_3_ac[0][0]                \n",
      "                                                                 block8_4_conv[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block8_4_ac (Activation)        (None, 8, 8, 2080)   0           block8_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_180 (Conv2D)             (None, 8, 8, 192)    399360      block8_4_ac[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_180 (BatchN (None, 8, 8, 192)    576         conv2d_180[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_180 (Activation)     (None, 8, 8, 192)    0           batch_normalization_180[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_181 (Conv2D)             (None, 8, 8, 224)    129024      activation_180[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_181 (BatchN (None, 8, 8, 224)    672         conv2d_181[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_181 (Activation)     (None, 8, 8, 224)    0           batch_normalization_181[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_179 (Conv2D)             (None, 8, 8, 192)    399360      block8_4_ac[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_182 (Conv2D)             (None, 8, 8, 256)    172032      activation_181[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_179 (BatchN (None, 8, 8, 192)    576         conv2d_179[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_182 (BatchN (None, 8, 8, 256)    768         conv2d_182[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_179 (Activation)     (None, 8, 8, 192)    0           batch_normalization_179[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_182 (Activation)     (None, 8, 8, 256)    0           batch_normalization_182[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block8_5_mixed (Concatenate)    (None, 8, 8, 448)    0           activation_179[0][0]             \n",
      "                                                                 activation_182[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block8_5_conv (Conv2D)          (None, 8, 8, 2080)   933920      block8_5_mixed[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block8_5 (Lambda)               (None, 8, 8, 2080)   0           block8_4_ac[0][0]                \n",
      "                                                                 block8_5_conv[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block8_5_ac (Activation)        (None, 8, 8, 2080)   0           block8_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_184 (Conv2D)             (None, 8, 8, 192)    399360      block8_5_ac[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_184 (BatchN (None, 8, 8, 192)    576         conv2d_184[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_184 (Activation)     (None, 8, 8, 192)    0           batch_normalization_184[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_185 (Conv2D)             (None, 8, 8, 224)    129024      activation_184[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_185 (BatchN (None, 8, 8, 224)    672         conv2d_185[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_185 (Activation)     (None, 8, 8, 224)    0           batch_normalization_185[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_183 (Conv2D)             (None, 8, 8, 192)    399360      block8_5_ac[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_186 (Conv2D)             (None, 8, 8, 256)    172032      activation_185[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_183 (BatchN (None, 8, 8, 192)    576         conv2d_183[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_186 (BatchN (None, 8, 8, 256)    768         conv2d_186[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_183 (Activation)     (None, 8, 8, 192)    0           batch_normalization_183[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_186 (Activation)     (None, 8, 8, 256)    0           batch_normalization_186[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block8_6_mixed (Concatenate)    (None, 8, 8, 448)    0           activation_183[0][0]             \n",
      "                                                                 activation_186[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block8_6_conv (Conv2D)          (None, 8, 8, 2080)   933920      block8_6_mixed[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block8_6 (Lambda)               (None, 8, 8, 2080)   0           block8_5_ac[0][0]                \n",
      "                                                                 block8_6_conv[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block8_6_ac (Activation)        (None, 8, 8, 2080)   0           block8_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_188 (Conv2D)             (None, 8, 8, 192)    399360      block8_6_ac[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_188 (BatchN (None, 8, 8, 192)    576         conv2d_188[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_188 (Activation)     (None, 8, 8, 192)    0           batch_normalization_188[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_189 (Conv2D)             (None, 8, 8, 224)    129024      activation_188[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_189 (BatchN (None, 8, 8, 224)    672         conv2d_189[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_189 (Activation)     (None, 8, 8, 224)    0           batch_normalization_189[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_187 (Conv2D)             (None, 8, 8, 192)    399360      block8_6_ac[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_190 (Conv2D)             (None, 8, 8, 256)    172032      activation_189[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_187 (BatchN (None, 8, 8, 192)    576         conv2d_187[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_190 (BatchN (None, 8, 8, 256)    768         conv2d_190[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_187 (Activation)     (None, 8, 8, 192)    0           batch_normalization_187[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_190 (Activation)     (None, 8, 8, 256)    0           batch_normalization_190[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block8_7_mixed (Concatenate)    (None, 8, 8, 448)    0           activation_187[0][0]             \n",
      "                                                                 activation_190[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block8_7_conv (Conv2D)          (None, 8, 8, 2080)   933920      block8_7_mixed[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block8_7 (Lambda)               (None, 8, 8, 2080)   0           block8_6_ac[0][0]                \n",
      "                                                                 block8_7_conv[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block8_7_ac (Activation)        (None, 8, 8, 2080)   0           block8_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_192 (Conv2D)             (None, 8, 8, 192)    399360      block8_7_ac[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_192 (BatchN (None, 8, 8, 192)    576         conv2d_192[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_192 (Activation)     (None, 8, 8, 192)    0           batch_normalization_192[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_193 (Conv2D)             (None, 8, 8, 224)    129024      activation_192[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_193 (BatchN (None, 8, 8, 224)    672         conv2d_193[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_193 (Activation)     (None, 8, 8, 224)    0           batch_normalization_193[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_191 (Conv2D)             (None, 8, 8, 192)    399360      block8_7_ac[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_194 (Conv2D)             (None, 8, 8, 256)    172032      activation_193[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_191 (BatchN (None, 8, 8, 192)    576         conv2d_191[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_194 (BatchN (None, 8, 8, 256)    768         conv2d_194[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_191 (Activation)     (None, 8, 8, 192)    0           batch_normalization_191[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_194 (Activation)     (None, 8, 8, 256)    0           batch_normalization_194[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block8_8_mixed (Concatenate)    (None, 8, 8, 448)    0           activation_191[0][0]             \n",
      "                                                                 activation_194[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block8_8_conv (Conv2D)          (None, 8, 8, 2080)   933920      block8_8_mixed[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block8_8 (Lambda)               (None, 8, 8, 2080)   0           block8_7_ac[0][0]                \n",
      "                                                                 block8_8_conv[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block8_8_ac (Activation)        (None, 8, 8, 2080)   0           block8_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_196 (Conv2D)             (None, 8, 8, 192)    399360      block8_8_ac[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_196 (BatchN (None, 8, 8, 192)    576         conv2d_196[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_196 (Activation)     (None, 8, 8, 192)    0           batch_normalization_196[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_197 (Conv2D)             (None, 8, 8, 224)    129024      activation_196[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_197 (BatchN (None, 8, 8, 224)    672         conv2d_197[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_197 (Activation)     (None, 8, 8, 224)    0           batch_normalization_197[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_195 (Conv2D)             (None, 8, 8, 192)    399360      block8_8_ac[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_198 (Conv2D)             (None, 8, 8, 256)    172032      activation_197[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_195 (BatchN (None, 8, 8, 192)    576         conv2d_195[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_198 (BatchN (None, 8, 8, 256)    768         conv2d_198[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_195 (Activation)     (None, 8, 8, 192)    0           batch_normalization_195[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_198 (Activation)     (None, 8, 8, 256)    0           batch_normalization_198[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block8_9_mixed (Concatenate)    (None, 8, 8, 448)    0           activation_195[0][0]             \n",
      "                                                                 activation_198[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block8_9_conv (Conv2D)          (None, 8, 8, 2080)   933920      block8_9_mixed[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block8_9 (Lambda)               (None, 8, 8, 2080)   0           block8_8_ac[0][0]                \n",
      "                                                                 block8_9_conv[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block8_9_ac (Activation)        (None, 8, 8, 2080)   0           block8_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_200 (Conv2D)             (None, 8, 8, 192)    399360      block8_9_ac[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_200 (BatchN (None, 8, 8, 192)    576         conv2d_200[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_200 (Activation)     (None, 8, 8, 192)    0           batch_normalization_200[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_201 (Conv2D)             (None, 8, 8, 224)    129024      activation_200[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_201 (BatchN (None, 8, 8, 224)    672         conv2d_201[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_201 (Activation)     (None, 8, 8, 224)    0           batch_normalization_201[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_199 (Conv2D)             (None, 8, 8, 192)    399360      block8_9_ac[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_202 (Conv2D)             (None, 8, 8, 256)    172032      activation_201[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_199 (BatchN (None, 8, 8, 192)    576         conv2d_199[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_202 (BatchN (None, 8, 8, 256)    768         conv2d_202[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_199 (Activation)     (None, 8, 8, 192)    0           batch_normalization_199[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_202 (Activation)     (None, 8, 8, 256)    0           batch_normalization_202[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block8_10_mixed (Concatenate)   (None, 8, 8, 448)    0           activation_199[0][0]             \n",
      "                                                                 activation_202[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block8_10_conv (Conv2D)         (None, 8, 8, 2080)   933920      block8_10_mixed[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block8_10 (Lambda)              (None, 8, 8, 2080)   0           block8_9_ac[0][0]                \n",
      "                                                                 block8_10_conv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv_7b (Conv2D)                (None, 8, 8, 1536)   3194880     block8_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv_7b_bn (BatchNormalization) (None, 8, 8, 1536)   4608        conv_7b[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_7b_ac (Activation)         (None, 8, 8, 1536)   0           conv_7b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "avg_pool (GlobalAveragePooling2 (None, 1536)         0           conv_7b_ac[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "predictions (Dense)             (None, 58)           89146       avg_pool[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 54,425,882\n",
      "Trainable params: 54,365,338\n",
      "Non-trainable params: 60,544\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras import layers, models, Input\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Conv2D, Dense, Flatten, Dropout,BatchNormalization,Activation\n",
    "from tensorflow.keras.layers import MaxPooling2D, AveragePooling2D, Concatenate, Lambda,GlobalAveragePooling2D\n",
    "from tensorflow.keras import backend as K\n",
    "\n",
    "def conv2d_bn(x,filters,kernel_size,strides=1,padding='same',activation='relu',use_bias=False,name=None):\n",
    "    \n",
    "    x = Conv2D(filters,kernel_size,strides=strides,padding=padding,use_bias=use_bias,name=name)(x)\n",
    "    \n",
    "    if not use_bias:\n",
    "        bn_axis = 1 if K.image_data_format() == 'channels_first' else 3\n",
    "        bn_name = None if name is None else name + '_bn'\n",
    "        x = BatchNormalization(axis=bn_axis, scale=False, name=bn_name)(x)\n",
    "    if activation is not None:\n",
    "        ac_name = None if name is None else name + '_ac'\n",
    "        x = Activation(activation, name=ac_name)(x)\n",
    "    return x\n",
    "\n",
    "def inception_resnet_block(x, scale, block_type, block_idx, activation='relu'):\n",
    "    if block_type == 'block35':\n",
    "        branch_0 = conv2d_bn(x, 32, 1)\n",
    "        branch_1 = conv2d_bn(x, 32, 1)\n",
    "        branch_1 = conv2d_bn(branch_1, 32, 3)\n",
    "        branch_2 = conv2d_bn(x, 32, 1)\n",
    "        branch_2 = conv2d_bn(branch_2, 48, 3)\n",
    "        branch_2 = conv2d_bn(branch_2, 64, 3)\n",
    "        branches = [branch_0, branch_1, branch_2]\n",
    "    elif block_type == 'block17':\n",
    "        branch_0 = conv2d_bn(x, 192, 1)\n",
    "        branch_1 = conv2d_bn(x, 128, 1)\n",
    "        branch_1 = conv2d_bn(branch_1, 160, [1, 7])\n",
    "        branch_1 = conv2d_bn(branch_1, 192, [7, 1])\n",
    "        branches = [branch_0, branch_1]\n",
    "    elif block_type == 'block8':\n",
    "        branch_0 = conv2d_bn(x, 192, 1)\n",
    "        branch_1 = conv2d_bn(x, 192, 1)\n",
    "        branch_1 = conv2d_bn(branch_1, 224, [1, 3])\n",
    "        branch_1 = conv2d_bn(branch_1, 256, [3, 1])\n",
    "        branches = [branch_0, branch_1]\n",
    "    else:\n",
    "        raise ValueError('Unknown Inception-ResNet block type. '\n",
    "                         'Expects \"block35\", \"block17\" or \"block8\", '\n",
    "                         'but got: ' + str(block_type))\n",
    "\n",
    "    block_name = block_type + '_' + str(block_idx)\n",
    "    mixed = Concatenate(name=block_name + '_mixed')(branches)\n",
    "    up = conv2d_bn(mixed,K.int_shape(x)[3],1,activation=None,use_bias=True,name=block_name + '_conv')\n",
    "\n",
    "    x = Lambda(lambda inputs, scale: inputs[0] + inputs[1] * scale,\n",
    "               output_shape=K.int_shape(x)[1:],\n",
    "               arguments={'scale': scale},\n",
    "               name=block_name)([x, up])\n",
    "    if activation is not None:\n",
    "        x = Activation(activation, name=block_name + '_ac')(x)\n",
    "    return x\n",
    "\n",
    "\n",
    "def InceptionResNetV2(input_shape=[299,299,3],classes=1000):\n",
    "\n",
    "    inputs = Input(shape=input_shape)\n",
    "\n",
    "    # Stem block\n",
    "    x = conv2d_bn(inputs, 32, 3, strides=2, padding='valid')\n",
    "    x = conv2d_bn(x, 32, 3, padding='valid')\n",
    "    x = conv2d_bn(x, 64, 3)\n",
    "    x = MaxPooling2D(3, strides=2)(x)\n",
    "    x = conv2d_bn(x, 80, 1, padding='valid')\n",
    "    x = conv2d_bn(x, 192, 3, padding='valid')\n",
    "    x = MaxPooling2D(3, strides=2)(x)\n",
    "\n",
    "    # Mixed 5b (Inception-A block)\n",
    "    branch_0 = conv2d_bn(x, 96, 1)\n",
    "    branch_1 = conv2d_bn(x, 48, 1)\n",
    "    branch_1 = conv2d_bn(branch_1, 64, 5)\n",
    "    branch_2 = conv2d_bn(x, 64, 1)\n",
    "    branch_2 = conv2d_bn(branch_2, 96, 3)\n",
    "    branch_2 = conv2d_bn(branch_2, 96, 3)\n",
    "    branch_pool = AveragePooling2D(3, strides=1, padding='same')(x)\n",
    "    branch_pool = conv2d_bn(branch_pool, 64, 1)\n",
    "    branches = [branch_0, branch_1, branch_2, branch_pool]\n",
    "\n",
    "    x = Concatenate(name='mixed_5b')(branches)\n",
    "\n",
    "    # 10次 Inception-ResNet-A block\n",
    "    for block_idx in range(1, 11):\n",
    "        x = inception_resnet_block(x, scale=0.17, block_type='block35', block_idx=block_idx)\n",
    "\n",
    "    # Reduction-A block\n",
    "    branch_0 = conv2d_bn(x, 384, 3, strides=2, padding='valid')\n",
    "    branch_1 = conv2d_bn(x, 256, 1)\n",
    "    branch_1 = conv2d_bn(branch_1, 256, 3)\n",
    "    branch_1 = conv2d_bn(branch_1, 384, 3, strides=2, padding='valid')\n",
    "    branch_pool = MaxPooling2D(3, strides=2, padding='valid')(x)\n",
    "    branches = [branch_0, branch_1, branch_pool]\n",
    "    x = Concatenate(name='mixed_6a')(branches)\n",
    "\n",
    "    # 20次 Inception-ResNet-B block\n",
    "    for block_idx in range(1, 21):\n",
    "        x = inception_resnet_block(x, scale=0.1, block_type='block17', block_idx=block_idx)\n",
    "\n",
    "\n",
    "    # Reduction-B block\n",
    "    branch_0 = conv2d_bn(x, 256, 1)\n",
    "    branch_0 = conv2d_bn(branch_0, 384, 3, strides=2, padding='valid')\n",
    "    branch_1 = conv2d_bn(x, 256, 1)\n",
    "    branch_1 = conv2d_bn(branch_1, 288, 3, strides=2, padding='valid')\n",
    "    branch_2 = conv2d_bn(x, 256, 1)\n",
    "    branch_2 = conv2d_bn(branch_2, 288, 3)\n",
    "    branch_2 = conv2d_bn(branch_2, 320, 3, strides=2, padding='valid')\n",
    "    branch_pool = MaxPooling2D(3, strides=2, padding='valid')(x)\n",
    "    branches = [branch_0, branch_1, branch_2, branch_pool]\n",
    "    x = Concatenate(name='mixed_7a')(branches)\n",
    "\n",
    "    # 10次 Inception-ResNet-C block\n",
    "    for block_idx in range(1, 10):\n",
    "        x = inception_resnet_block(x, scale=0.2, block_type='block8', block_idx=block_idx)\n",
    "    x = inception_resnet_block(x, scale=1., activation=None, block_type='block8', block_idx=10)\n",
    "\n",
    "\n",
    "    x = conv2d_bn(x, 1536, 1, name='conv_7b')\n",
    "\n",
    "    x = GlobalAveragePooling2D(name='avg_pool')(x)\n",
    "    x = Dense(classes, activation='softmax', name='predictions')(x)\n",
    "\n",
    "    # 创建模型\n",
    "    model = Model(inputs, x, name='inception_resnet_v2')\n",
    "\n",
    "    return model\n",
    "\n",
    "model = InceptionResNetV2([299,299,3],58)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.官方模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import tensorflow as tf\n",
    "# # 如果使用官方模型需要将图片shape调整为 [299,299,3]，目前图片的shape是 [150,150,3]\n",
    "# model = tf.keras.applications.inception_resnet_v2.InceptionResNetV2()\n",
    "# model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 五、设置动态学习率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里先罗列一下学习率大与学习率小的优缺点。\n",
    "\n",
    "- 学习率大\n",
    "    - 优点：\n",
    "        1、加快学习速率。\n",
    "        2、有助于跳出局部最优值。\n",
    "    - 缺点：\n",
    "        1、导致模型训练不收敛。\n",
    "        2、单单使用大学习率容易导致模型不精确。\n",
    "\n",
    "- 学习率小\n",
    "    - 优点：\n",
    "        1、有助于模型收敛、模型细化。\n",
    "        2、提高模型精度。\n",
    "    - 缺点：\n",
    "        1、很难跳出局部最优值。\n",
    "        2、收敛缓慢。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意：这里设置的动态学习率为：指数衰减型（ExponentialDecay）。在每一个epoch开始前，学习率（learning_rate）都将会重置为初始学习率（initial_learning_rate），然后再重新开始衰减。计算公式如下："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">learning_rate = initial_learning_rate * decay_rate ^ (step / decay_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置初始学习率\n",
    "initial_learning_rate = 1e-4\n",
    "\n",
    "lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(\n",
    "        initial_learning_rate, \n",
    "        decay_steps=100,      # 敲黑板！！！这里是指 steps，不是指epochs\n",
    "        decay_rate=0.96,  # lr经过一次衰减就会变成 decay_rate*lr\n",
    "        staircase=True)\n",
    "\n",
    "# 将指数衰减学习率送入优化器\n",
    "optimizer = tf.keras.optimizers.Adam(learning_rate=lr_schedule)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 六、编译"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在准备对模型进行训练之前，还需要再对其进行一些设置。以下内容是在模型的编译步骤中添加的：\n",
    "\n",
    "- 损失函数（loss）：用于衡量模型在训练期间的准确率。\n",
    "- 优化器（optimizer）：决定模型如何根据其看到的数据和自身的损失函数进行更新。\n",
    "- 指标（metrics）：用于监控训练和测试步骤。以下示例使用了准确率，即被正确分类的图像的比率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=optimizer,\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 七、训练模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Inception-ResNet-v2` 模型相对之前的模型较为复杂，故而运行耗时也更长，我这边每一个epoch运行时间是130s左右。我的GPU配置是 `NVIDIA GeForce RTX 3080`。建议大家先将 `epochs` 调整为1跑通程序。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "834/834 [==============================] - 154s 163ms/step - loss: 2.5214 - accuracy: 0.3563 - val_loss: 1.3834 - val_accuracy: 0.6168\n",
      "Epoch 2/10\n",
      "834/834 [==============================] - 133s 159ms/step - loss: 0.9230 - accuracy: 0.7522 - val_loss: 0.5457 - val_accuracy: 0.8531\n",
      "Epoch 3/10\n",
      "834/834 [==============================] - 133s 159ms/step - loss: 0.3952 - accuracy: 0.9105 - val_loss: 0.3391 - val_accuracy: 0.9064\n",
      "Epoch 4/10\n",
      "834/834 [==============================] - 134s 160ms/step - loss: 0.1876 - accuracy: 0.9655 - val_loss: 0.2481 - val_accuracy: 0.9296\n",
      "Epoch 5/10\n",
      "834/834 [==============================] - 131s 156ms/step - loss: 0.1071 - accuracy: 0.9862 - val_loss: 0.1265 - val_accuracy: 0.9716\n",
      "Epoch 6/10\n",
      "834/834 [==============================] - 128s 153ms/step - loss: 0.0587 - accuracy: 0.9954 - val_loss: 0.0911 - val_accuracy: 0.9794\n",
      "Epoch 7/10\n",
      "834/834 [==============================] - 132s 158ms/step - loss: 0.0429 - accuracy: 0.9976 - val_loss: 0.0941 - val_accuracy: 0.9777\n",
      "Epoch 8/10\n",
      "834/834 [==============================] - 132s 158ms/step - loss: 0.0306 - accuracy: 0.9980 - val_loss: 0.0955 - val_accuracy: 0.9777\n",
      "Epoch 9/10\n",
      "834/834 [==============================] - 133s 158ms/step - loss: 0.0248 - accuracy: 0.9997 - val_loss: 0.0864 - val_accuracy: 0.9794\n",
      "Epoch 10/10\n",
      "834/834 [==============================] - 132s 158ms/step - loss: 0.0216 - accuracy: 0.9988 - val_loss: 0.0750 - val_accuracy: 0.9794\n"
     ]
    }
   ],
   "source": [
    "epochs = 10\n",
    "\n",
    "history = model.fit(\n",
    "    train_ds,\n",
    "    validation_data=val_ds,\n",
    "    epochs=epochs\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 八、模型评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs_range = range(epochs)\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.suptitle(\"微信公众号(K同学啊)中回复（DL+14）可获取数据and模型\")\n",
    "\n",
    "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
    "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(epochs_range, loss, label='Training Loss')\n",
    "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 九、模型的保存与加载"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存模型\n",
    "model.save('model/14_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载模型\n",
    "new_model = keras.models.load_model('model/14_model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 十、预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 采用加载的模型（new_model）来看预测结果\n",
    "\n",
    "plt.figure(figsize=(10, 5))  # 图形的宽为10高为5\n",
    "plt.suptitle(\"微信公众号(K同学啊)中回复（DL+14）可获取数据and模型\")\n",
    "\n",
    "for images, labels in val_ds.take(1):\n",
    "    for i in range(6):\n",
    "        ax = plt.subplot(2, 3, i + 1)  \n",
    "        \n",
    "        # 显示图片\n",
    "        plt.imshow(images[i])\n",
    "        \n",
    "        # 需要给图片增加一个维度\n",
    "        img_array = tf.expand_dims(images[i], 0) \n",
    "        \n",
    "        # 使用模型预测路标\n",
    "        predictions = new_model.predict(img_array)\n",
    "        plt.title(np.argmax(predictions))\n",
    "\n",
    "        plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**推荐阅读：**\n",
    "\n",
    "✨ [**深度学习100例-卷积神经网络（CNN）实现mnist手写数字识别 | 第1天**](https://mtyjkh.blog.csdn.net/article/details/116920825) \n",
    "\n",
    "✨ [**深度学习100例 - 卷积神经网络（Inception V3）识别手语 | 第13天**](https://mtyjkh.blog.csdn.net/article/details/118310170)\n",
    "\n",
    "✨ [**手把手教你用 CNN 识别验证码 - 深度学习100例 | 第12天**](https://mtyjkh.blog.csdn.net/article/details/118152545)\n",
    "\n",
    "✨ [**循环神经网络（LSTM）实现股票预测-深度学习100例 | 第10天**](https://mtyjkh.blog.csdn.net/article/details/117907074)\n",
    "\n",
    "✨ [**深度学习100例-卷积神经网络（VGG-16）识别海贼王草帽一伙 | 第6天**](https://mtyjkh.blog.csdn.net/article/details/117331631)\n",
    "\n",
    "\n",
    "**🚀 来自专栏：[《深度学习100例》](https://blog.csdn.net/qq_38251616/category_11068756.html)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**未完～**\n",
    "\n",
    "**持续更新 欢迎 点赞👍、收藏⭐、关注👀**\n",
    "\n",
    "- 点赞👍：点赞给我持续更新的动力\n",
    "- 收藏⭐️：收藏后你能够随时找到文章\n",
    "- 关注👀：关注我第一时间接收最新文章"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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